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💎THE RESULTS: r/RomanceClub Community Survey!💎

💎THE RESULTS: RomanceClub Community Survey!💎
First of all, thank you very much to everyone who took part in the second ever Reddit Romance Club community survey! We mods were absolutely amazed by the high number of responses, so thank you for making this such a vibrant and engaged community! You all rock.
After grinding the (many) numbers, here are the results, which we hope you will find as interesting as we did.
Just a note: this survey was opened at the end of May and closed shortly after the June release, hence its questions only barely included Legend of the Willow and did not include Dracula: a Love Story. For this reason, we have not counted the (very few) replies that have been given in the "other" boxes mentioning characters that were not yet available as Lis/known as LIs in the May release (think Leo, Vlad, Kazu etc) as this would have not been fair to those who had answered the survey before the June update.
Having said that... buckle up for the ride! Lots of interesting info ahead.
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💎Question 1: Which RC story is your favourite?

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No one will be surprised to find out that Heaven's Secret is the top story in this community right now, chosen by over 45% of the respondents. Our nostalgic heart is very happy, however, to see some old favs still make the podium - albeit trailing significantly behind. Moonborn and Shadows of Saintfour score second and third place, only separated by a handful of votes at around 11%, but newer release Chasing You is already breathing on their neck at 10.7%.
A healthy mix of new and old stories follows: Sails in the fog is in fifth place with 7.8% of the preferences, while Legend of the willow, after only a few episodes, already scores a very good sixth place, in a tie with Seduced by the rhythm at 4.3% of the votes. Queen in 30 days is seventh with 3.5% and My Hollywood Story is eighth with 1.2%.
Last place goes to Wave Patrol at 0.4%, which sadly doesn't come as a shock given the general feeling that the romantic/reputation points system was too complicated.
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💎Question 2: Who are your favourite LIs?

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HS being the most popular story unsurprisingly propels some of its main LIs onto the podium with supersonic speed.
Bad boys rule, with Lucifer taking the crown with a whopping 65.7% of the votes and Alexander (CY) taking silver at 49.3%. The nice guys are not too far behind, though, with Dino in third place at 47.4% and Max (MB) only just about missing the podium in fourth place at 45.3%. We go back to bad boys with Brandon (SBR) in fifth, but soft spoken Sam (CY) is ready to balance things out again with his sixth place.
The most surprising result on top of the rankings is Jake (WP) who makes the top ten with a very healthy seventh place. He is really hard work, but obviously we all think he's totally worth it!
Old favourites Michael (SOS) and Victor (MB) still hold onto the hearts of their fans by scoring eight and ninth place respectively. First among the women - and the only female LI to make the top 10 - is the delightfully devilish Mimi (HS).
Waves' mate Sebastian misses the top ten only by a hair, placing himself in 11th place with a healthy 20.7%. Bodyguard Adam is the most favourite LI in Q30 in 12th place, followed by a row of SOS boys, with John, Derek and Aaron scoring very similar percentages in 13th, 14th and 15th place respectively. Sweetheart Ray is no longer the most favourite LI to come out of MHS, as in this round he ends up in a tie for 16th place with none other than his almost polar opposite, rough and ready Captain Jeff.
Leonard from Q30 (17th place) ties with Cherry from SOS but at least he beats his brother Richard (20th place) in the heart of the readers - and we all know that he'd be pretty pleased with that. Claire (SBR) is the second most favourite female LI in 18th place, while mysterious Luke (SOS) completes the top 20 in 19th place.
Here are the rest of the Lis who placed lower than the top 20:
(21) Carlos (SBR) 9.2%
(22) Justin (SBR) 8.6%
(23) Benny Bart (MB) 8.4%
(24) Tarino (MHS) 8.1%
(25) Gino (MHS) tied with Stephanie (SOS) at 7.8%
(26) Dante (MB) 6.9%
(27) Andy (HS) 6.3%
(28) Mike (MHS) 6.1 %
(29) Alek (WP) tied with Dante (CY) at 5.9%
(30) Kayla (WP) 5.3 %
(31) Alex (MHS) 3.9%
(32) Chris (SIF) 3.4%
(33) Frances (MB) 3.2%
(34) William (SIF) 3.1%
(35) Trisha (MB) 2.6%
(36) Charles (SBR) 2.1%
(37) Orlando (SBR) 1.8%
(38) Chris the bodyguard (MHS) tied with Adi (HS) at 1.6%
(39) Ellen (MHS) tied with Manta (SIF) at 1.2%
(40) Masked Man (SOS) 1.1%
(41) Ellia (CY) 0.8%
(42) Mermaid (SIF) 0.6%
(43) Simon (MB) 0.4%
(44) Charles (WP) tied with Emma (Q30) and Jackie (SIF) at 0.2%.
These lower rankings include some LIs that, based on the discussions we see on the subreddit, we were not expecting to get as many votes as they did - and vice versa. Dante from CY has more votes than Orlando from SBR? And Chris the bodyguard (MHS) beat the Masked Man (SOS)? Say what... Also: Jackie (SIF) definitely deserved a lot more votes! We might have to start a hashtag or something.
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💎Question 3: Which non-LI character you’d romance in a heartbeat?

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Here are the top 15 most desired LIs in this community:
To absolutely no one's surprise, Geralt (HS) takes the top spot with 39.1% of the readers eager to unbuckle his sexy neck belts at the first occasion. Dreamy Xander from MB takes a very respectable second place with 23.2%: we will forever long for his full lips and crisp linen suit. Another MB favourite, Prince Ethan Wood completes the podium with his Matrix-style coat and intense eyes, but sassy and sexy demon Austie (HS) is not very far behind in fourth place.
Vampires Dustin Chase in fifth place and - although at quite a distance - Sophia in sixth join the ranks of the many LIs who sadly never were in MB. Cute lifeguard Zoey from WP ranks seventh, and no worries if you don't remember who she is: her screen time was about 5 minutes total - but enough to end up in a tie with angelic mentor Misselina from HS. Frenemy Candy from SOS makes eighth place, while evil stunner Monica from MB clutches ninth. To complete the top 10 is no one else but grumpy Angel Fencio (HS) - we obviously all want him to show us his collection of talismans - tied with Bean from MHS, who sadly had the audacity to get married to someone else.
In 11th place is SOS great friend Bobby, whose bravery in the face of untold horrors gave him a special place in all our hearts, in a tie with another WP lifeguard, Ryan (yeah, we have little recollection of him as well). Party-loving and OSHA nemesis Anthony Wood (MHS) is in 12th place, while scheming yet gorgeous Julia (Q30) takes 13th.
In 14th place is no one else but our dear Sailor Bobby - an option that was added as humorous but instead raked up a fairly respectable 14% of votes. As they say, if you are not handsome you should be handy, and no one is a better dress maker than Bobby! Plus, how can we forget when he disguised himself as a tribesman to save Adelaide from becoming soup? He ends up in a tie with a fan favourite, sweet angel Sammy (HS). Completing the top-15 is another HS angel, the ethereal Leeloo.
This question also had an "other" box, where people could add names that were not included in the list. For all those (quite a few!) people who wrote Dino (HS), Sam (CY) and Orlando (SBR)... we choose to believe you misread the question, but if you didn't... oh boy, have we got good news for you!
A few people also wished for Rachel (CY) and Hiro (SBR) to be LIs, so that's another happy ending there as per the latest release.
Some also wished for Diego, Baron Samedi and Jackie from SIF, and Joseph, Christian and Gustavo from SBR to be LIs, and we are happy to say that, although their routes might be a bit hidden and not all of them can be endgame LIs, you can most definitely already hookup with/romance all of them. Check the wiki for details!
A few people asked for the coffee shop owner in CY... we have the feeling that we know who at least one of them is, and truth be told, that beard is dreamy so we can see their point! More bearded LIs please!
Those who asked for Fyr... far from us to kink shame here, but let's just hope he turns out to be human at some point! We also have some Seraph Crowley (HS) and Angel Mora (MB) fans amongst us, as clear proof that no one is ever too old for love, plus WP Agent Phillips' manbun has also scored him some eager fans.
But that one person who asked for Sean from MB... we hope for your sake you are also about 12 years old because otherwise you need an old priest, a young priest and also a police officer.
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💎Question 4: Which LI do you think is overrated, and why?

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Here are the top 10 most overrated LIs in the game according to our community.
You know how they say never rest on your laurels? In a surprising (or maybe not?) twist of fate, some of the most liked LIs also topped the most overrated rankings, which goes to show that the beauty of our community is that we all have different (and sometimes opposite) opinions! So please let's not fight in the comments, haha.
The most overrated LI crown goes to Lucifer with the 21.9% of the votes, (which is almost as him winning an Oscar and a Razzie on the same evening) mostly due to his behaviour, which many identify as "toxic", "abusive" and "triggering". Many readers are "not comfortable with his choking/manhandling of MC", and his "lack of respect for personal space". "Being treated poorly in the hope to finally reach a hidden soft side does not seem worth it". Some think "he needs therapy", and wonder "why he's still behaving like a teen while he's possibly thousands of years old". A reason why many dislike him however, is also "the daily flood of fanart that features him": we might all be a little Lucifered-out here on the subreddit!
Tied in second place (pun fully intended!) are Alexander (CY) and Victor (MB) at 11.5%: the reasons given for both of them are surprisingly similar. Both boys are into BDSM but neither seem to "truly know the rules of consent" and people think that they "overstep boundaries a little too often". Both have been described as "creepy", "controlling" and "plain weird". Victor is also guilty of being "boring" ("I asked for a tea not for your life story in India!" - someone wrote). Both have been invited to "drop the Christian Grey act" and some people think "they would be arrested in real life if they acted this way". Oh boy.
Justin (SBR) completes this unflattering podium at 9.1% because of his "obnoxious outbursts" and the way he treats MC. He is "rude" and "mean" and people seem to be willing to "pay diamonds to put him in his place". Hopefully that won't be necessary!
Jake from WP is fourth at 7.8%, the main reason being that he is "too difficult to romance", "too expensive and still rude", and that "we have to solve the Da Vinci code to get him" - as someone hilariously wrote.
Bad boy Brandon (SBR) scores 6.1% of the votes landing fifth place, with the word "jerk" being the most recurrently used to describe him. He is "arrogant", a "vanishing act", and "he is never nice to MC for long". Come on, Brandon! You can do better!
Unclaimed Andy (HS) takes sixth place with 4.5% for being "jealous" and "annoying" - although we would maybe argue that he's not really that overrated, as far as we can see from the sub...
In seventh place is Max (MB) at 4.1% but we are confused by the person who mentioned "his abs being too perfect" as a reason for disliking him. Of course, there is such a thing as too much of a good thing, so... fair enough? Other words used are "too boring", otherwise many people voted for him but did not really give a reason why. Max needs to work on his PR clearly!
Another tie in eight place sees Adam (Q30) and Dino (HS) score 3.7% of the votes. The Royal bodyguard is described as "a barbarian" and his behaviour as "possessive" and "controlling", while the main complaints against Dino seem for the vast majority to be directed to his looks: comments range from "his eyes look disproportionally big compared to his head" to "his hair seems separated from his face" to some people calling him a "Fabio lookalike". Beauty is in the eye of the beholder indeed!
Gruff Captain Jeff (MHS) makes ninth with 3.3%, mostly because of "the dodgy power dynamic between him and MC" and his "bullying": "I like puppies is not a free get out of jail card!" someone wrote. The fact that SOS Luke "drugged MC" bags him unanimously the tenth spot with 2.8% of the votes.
Not in the top ten but voted often enough to deserve a special mention are John (SOS) because of his "murderous tendencies", Derek (SOS) because "people only likes him for his glow-up", and Leonard (Q30) as "he took Emma's spot as the third main LI in the story" and "that was a cop out!" Plus "he seems so good only because the other two are the worst", someone quipped.
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💎Question 5: Which LI do you think is underrated, and why?

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Let's all cheer ourselves up with the opposite end of the spectrum! Here are the top 10 Lis that the community think deserve more love! The answers in this question were a lot more fragmented, with a lot of random characters getting very few votes, so the ranking percentages are significantly lower than in other questions.
A few people chose to write "every female LI" as this question's answer, and this is reflected in the rankings below, where way more female characters are mentioned compared to other questions. So RC, we need more screen time for badass, gorgeous, interesting female LIs!
HS still carries its weight as the most popular story, with three of its main LIs topping the rankings, all lamenting the fact that they are "unfairly overshadowed by bad boy Lucifer". Andy tops the list with 8.5% of the votes: players think he is "a really good guy", "sweet", "cute", "caring", "thoughtful". They admit "he has flaws" but he "will help if you need him" and "will stand up for those he cares about". It's nice to see him getting some love!
Devil cutie Mimi ends up as a close second with only a few votes of difference, at 8.1%. She is "cute", "badass", and "so cool". Many people wrote they don't usually romance female Lis but they chose her nonetheless because she is "a great LI in every way". Someone wishes RC would "flesh her out a little more" and "give her more screen time". Third spot is for Dino: a "sweetheart" and "the cutest man in the game".
Jake from WP nabs fourth place with the 4.9% of votes. Players thinks the focus is too much on how hard he is to pursue, while "he is totally worth it", because after the initial coldness he becomes "sweet", "kind" and "caring". His "love for his family is another big plus", and he is always "supportive", "mature", "loyal" and "intense". Someone also wrote that "his sex scenes are amazing".
Gorgeous dancer Carlos from SBR is in fifth place: he is described as "cute", "great personality", "respectful" and "the sweetest". One to watch for sure! Prince Leonard (Q30) ties with Claire (SBR) in sixth place. Leonard is "complicated", "interesting" and "clever", while Claire is "sweet", "mature" and "loyal". Seventh position is for Michael (SOS) - "cute", "affectionate", "funny" - and Kayla (WP) who's "really nice" and "one of the first female LIs that didn't seem like a complete afterthought".
Eight place goes to Sam (CY) - "wholesome", "the right amount of naughty and nice", "a sweet and likeable guy" - in a tie with Chris (SIF) - "funny", "strong", "loyal", "always has your back". Ninth place is another tie between Sebastian (SIF) - "sweet" and "supportive" - and Alex (MHS) - "amazing personality", "really helpful".
Last but not least the tenth place is a foursome: William (SIF) gets some love for being "good", "solid", "loyal" and "fun", in a tie with Charles (SBR) - described as "perfect", "romantic" and "caring", as well as "hot", "sexy" and "gentle" - Jackie (SIF) - "an under-appreciated king", "handsome" and "fun", and Frances (MB) - a "real badass" and "one of the best LIs in MB".
So, time to replay your favourite book and try out one of these Lis instead than your usual one!
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💎Question 6: If you could eat or drink one thing from the RC universe, what would you choose?

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Max (MB) might no longer hold the crown of most loved LI in the game, but his cooking skills still hold strong. A whopping 37.2% of the people in this community would eat anything he prepares. Getting drunk on Glyft at the HS Academy takes second spot with 23.1% of the preferences, while a sugar rush after a light BDSM session in CY is all what the 13.2% of us want, completing this delicious podium.
In fourth place is pizza with a bunch of MHS friends, fifth is potential death - as long as ice cream and Jake from WP are involved - and sixth is Anthony Wood's juice at one of his epic MHS parties.
Dinner at the SOS circus is seventh, chosen by a fearless 3% of the community, while canapés at a jewellery fashion show in Q30 score the eighth and last place.
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💎Question 7: If you could spend a weekend in any RC story, would you:

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An entire weekend in the RC universe! What mischief is our community planning to get up to?
It looks like HS is once again top of the list, with over 38% of players willing to test their wings and get some strange and possibly corrosive blue liquid down their unclaimed throats. But LOW's gorgeous backgrounds and atmospheric setting have convinced the 16% of us to go explore a Japanese village, and possibly meet some mysterious cutie. Adelaide and her SIF crew navigate steadily in third place: 10.7% of us would follow them over the edge of the world and beyond.
In fourth place is a spot of murder mystery fun in CY, as 10.3% of us would happily explore a British family mansion - bloodshed possible but not guaranteed. A diplomatic trip with the Q30 Sagar Royal Court appeals to the 6.1% of us, especially if a romantic sunset is on the bill. The quaint and frankly unsettling SOS woods do not scare the 5.7% of us, but as long as no one picks up a nice bouquet of flowers, we should all be ok. In seventh place is our favourite vampire popstar Benny Bart (MB) performing at the Taste of the Night, while eighth is a dance marathon in SBR, inclusive of a trip to romantic Paris. Tarino's somewhat unusual directorial skills in MHS score ninth place, while hot surfers in WP's Miami end up last.
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💎Question 8: If you could get more episodes of a series that has now ended, which one would you choose?

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It's time to go down memory lane! We loved all the stories that RC has now completed, but which one we miss the most?
Side note: SIF and WP were still ongoing when this survey was first opened hence they are not featured in this list.
Horror story SOS takes a clear lead, with over 47% of our community wishing we could get more adventures with MC and her friends. MB is second, with a healthy 34% of readers wishing to spend more time in the company of vampires and werewolves. Q30 is third, with 13.3% of readers missing its Royal Palace and all the intrigue coming with it, and last but not least is comedy MHS, which is missed by 5.4% of this community.
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And now, some questions about this community's gaming habits:
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💎Question 9: How do you usually approach LI relationships?

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This question had a fairly split response between those who date a few LIs but ultimately choose only one (48.9%) and those who are fiercely monogamous from the start (42.4%). A healthy 8.7% of the readers prefer instead to play the field and date as many LIs as the gameplay will allow. And with so many great characters to choose from, that's hardly a surprise!
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💎Question 10: Would you play a book that has a male MC?

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We all know that at the moment all RC stories are gender-locked with a female MC. But what does the community think? Would we play a book with a male MC? The majority is in favour, with 61.9% of the responders answering with a resounding YES.
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💎Question 11: Do you use the RC wiki on Fandom?

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Our amazing fan-written Romance Club wiki recently celebrated 100 pages!
It sounds like a whopping 78.8% of this community uses the wiki, while about 14.1% did not know it existed (so we hope you are using it now!) and 7.1% are true daredevils who play without any wiki help.
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💎Question 12: What genre of story do you enjoy the most?

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With new stories always coming up, we were curious to know which genres this community enjoys the most.
Despite the game being called Romance Club, the top of the genre ranking goes to Fantasy, with a striking 74.1% of preferences. But no worries: Romance is a steady second with a great 70.8% of the votes. Third place goes to Mystery with 65.7%.
Adventure comes fourth with 55.2%, followed by Horror (42.5%), Historical (35.1%), Science Fiction (29.1%) and finally Comedy (26.4%).
A very small number of people (too little to make percentage) also asked for drama, thriller, detective/crime, heist/spy, high school/teens, superheroes, zombies and time travel. All great ideas!
The community has spoken though: RC, give us elves and gnomes and medieval tales of debauchery and magic!
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💎Question 13: How long have you been playing Romance Club for?

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We were curious to know for how long we all have been playing this game we love. The survey showed a good mix of old and new readers, with a clear tendency towards long-term reading, which makes us so very happy to know we are all just equally addicted.
36% stated that they have been playing for over a year, 23.6% for more than six months, 17.8% for more than three months, 16.1% for more than one month and 6.4% for less than a month. Welcome one and all, we hope you are all going to be here for the long haul!
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💎Question 14: How did you find out about the game Romance Club?

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The main way in which our community has found out about RC is through the app store/google play store (70.9%). Another subreddit is a source for 13.5% of us (we probably have to say thank you to our friends at Lovestruck and Choices!) while a friend recommended the game to 8.7% of us.
Instagram (3%) and Facebook (1.5%) are also popular sources, but 2.4% of us arrived to the game through adverts, which is to us the most interesting data since in the mod team we haven't personally seen any adverts for this game - ever - so if anyone has screenshots, please post them in the comments, we are super curious!
Some users (too few to make percentage) also mentioned videos and memes on TikTok or Youtube, Google Search, Tumblr, Twitter, Vkontakte or even their own sister(s) as a source.
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💎Question 15: Which operating system do you play the game on?

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The majority of this community plays on Android (57.7%) while 42.3% play on iOS.
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💎Question 16: Which other story games do you play?

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Here are the top-10 story app games we play in this community, aside from RC.
Unsurprisingly, market leader Choices comes first with 53.3% of the votes. Another giant in the field, Episodes, comes second - although with quite a substantially smaller percentage of votes, clocking at 28.1%. The top-three is completed by UK TV show-inspired Love Island with the 24.6%.
Chapters is the fourth most played game at 24.2%, followed by Lovestruck and Love Sick - tied at 16.1%. Moments is sixth at 13.1%, new entry on the market Stories: Love and choices follows in seventh with 5.3%, Journeys is eighth with 4.7% and The Arcana is ninth with 2.6%. The top-ten is completed by Tabou Stories: Love Episodes in a tie with Originals - both at 1.2%.
Some also reminisced about Storyscapes (gone too soon but not forgotten!) and many other game apps were mentioned but by too few people to make up for an accountable percentage. We surely discovered some games we had never heard of before, though, including: Fictif, Heart's Choice, Everlasting Summer, Fancy Love, Romance: Stories and choices, Secrets: Game of choices, Fictions: Choose your emotions, Mystic Messenger, City of Love and many, many more... so thanks everyone for all these new suggestions!
And to that one person who selected half a dozen games and then commented with "it is a problem!" ... trust us, you are in very, very good company here!!
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And lastly, some demographics:
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💎Question 17: Where in the world are you from?

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We are a very international bunch, that's for sure! Here are the numbers:
45.5% of this community lives in Europe, 24.3% in North America, 16.9% is in Asia, 5.7% is in Central/South America, 5.3% is in Africa and 2.3% is in Australia/New Zealand. Welcome one and all! We are so happy you are here.
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💎Question 18: How old are you?

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How old are we? The survey has spoken: 44.7% is between the ages of 18 and 24; 28.1% is between 25 and 35; 21.1% is 17 or younger; 6.1 % is 36 or older.
We must admit that we did not expect so many people to be on the younger end of the spectrum! But we hope everyone - of all ages - will always find this subreddit to be a safe, welcoming and friendly place where to discuss this game we all love. We mods work hard every day to keep this the most relaxed and fun RC space on the net and we feel so lucky that you are all as awesome as you are!
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💎Question 19: What is your gender identity?

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The overwhelming majority of this community (93.5%) identifies as female, while 4.6% identifies as male, 1.3% is non-binary, 0,4% identifies as genderqueer and 0.2% marked themselves as confused.
The fact that MC is gender-locked female and that LGBTQ routes are limited in the game is certainly one of the reasons why our community is not more diverse. Hopefully RC will expand their stories to include more diverse gender choices in terms of MCs and LIs, so to allow more people to enjoy their great storytelling skills.
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💎Question 20: What is your sexual orientation?

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Here's the sexual orientation of our community:
70.1% identifies as Straight/Heterosexual
22.5% identifies as Bisexual
1.9% identifies as Lesbian/WLW
1.7% identifies as Pansexual
1.5% identifies as Gay/MLM
0.4% identifies as Aromantic
0.3% identifies as Aromantic/Bisexual
0.3% identifies as Asexual
0.3% identifies as Demisexual
0.2% identifies as Asexual/Biromantic
0.2% identifies as Asexual/Heterosexual
Once again, we hope that future plots featuring more diverse MC/LIs will attract more diverse players to our community.
----
That's all folks! We hope you found these results interesting and we look forward to a new survey once we hit 5000 users! Until then... happy gaming and thanks for making this awesome community as great as it is! :)
💎 RomanceClub mods 💎
💎u/LauraVi 💎u/swankytutu 💎u/directormmn
💎💎💎💎💎💎💎💎💎💎💎💎💎💎💎💎
submitted by LauraVi to RomanceClub [link] [comments]

2018 r/Fantasy Bingo Statistics

As I’ve done every year for the last couple years, I’ve done an overly in-depth look at all the cards submitted for the 2018 Reddit Fantasy Bingo Challenge. I am NOT an actual statistician, but I have once figured how much to tip in my head.
PRELIMINARY NOTES
Before I get into the numbers, here are some notes:
  1. I am not someone who determines of anyone gets a bingo, so when assembling this information, I don’t question a book you may have read or where you placed it on your bingo card.
  2. To make it easier for my analysis, I followed the idea of one book per square (or up to five for short stories). If you submitted the name of a series or an omnibus volume, I took only the first book in the series or omnibus (I didn’t do this in a couple minor cases, however). If you said you read Heartstrikers by Rachel Aaron, for example, I wrote down that you read Nice Dragons Finish Last so I could compare you against others who read only the first book.
  3. Graphic Novels: I subdivided the Graphic Novels/Audiobooks square into its component parts. It's possible that I made a mistake if you weren't clear that you were reading an audiobook versus a graphic novel (I hate everyone who read the comic of or listened to Rivers of London). I found it is more much useful to compare comic book series against each other instead of by volume, so the person who read Monstress Volume 1 was compared with one who read Monstress Volume 3.
  4. I attempted a gender breakdown, but I may be wrong! I said female/male/nonbinary/other based on the pronoun the authors preferred (author bios were useful in this regard), but sometimes I guessed. In a few rare occasions, I couldn't find evidence either way and left it alone. If you notice an error on my part, please let me know.
  5. I did not look to see if the author was a person of color or other demographic data such as language or country of origin or other interesting information. It took me about 60 hours to get the data to its current point, and with almost 1500 individual authors read, it’s far too much work for me to research.
  6. If you want to see my raw data, please click this link. I don’t include anyone’s username on this sheet. Though I only show the most popular books and authors per square below, I do have exactly how many people read what and whom, so if you’re curious about a specific author or book, feel free to ask in the comments!

PART I: What Is Popular?

Overall Bingo Cards
Most Read Books Overall:
  1. The Poppy War by R. F. Kuang was the most read book (64 times) (9.3% of all books)
  2. All Systems Red by Martha Wells (58 times)
  3. Circe by Madeline Miller (57 times).
  4. The Library at Mount Char by Scott Hawkins (53 times)
  5. The Long Way to a Small, Angry Planet by Becky Chambers (43 times)
The Poppy War was used on 9 different bingo squares. The book with the lowest ratio of number of times read to squares used (minimum 10 times used) was The Monster Baru Cormorant (11 times in 7 squares).
Most Authors Read Overall:
  1. Once again, Brandon Sanderson was the most read author (121 times) (17% of all authors)
  2. (tie) Naomi Novik & Terry Pratchett (98)
  3. Neil Gaiman (86)
  4. Becky Chambers (80)
  5. Martha Wells (72)
Brandon Sanderson was the most widely used author in 20 squares, followed by Neil Gaiman in 15 squares, and Naomi Novik, Terry Pratchett, Michael J. Sullivan, and N. K. Jemisin tied for 14 squares.
Random Note: Something I realized is that someone read a Roald Dahl book for this bingo... and it was the first ever in 4 years anyone had read a Dahl book before. It's always interesting what people do and do not read for Bingo versus their possible general popularity in the real world.
1. Novel that was Reviewed on Fantasy
Books:
  1. Kings of the Wyld by Nicholas Eames (7 times)
  2. (tie) Grey Sister by Mark Lawrence & Witchmark by C. L. Polk (4)
TOTAL: 268 books (204 unique)
LEFT BLANK: 14 / SUBSTITUTED: 9
*Authors: * 1. (tie) Josiah Bancroft & Mark Lawrence (9 times) 2. (tie) Brand don Sanderson & Nicholas Eames (7)
TOTAL: 272 authors (166 unique)
GENDER: 153 by men (56.3%) / 116 by women (42.6%) / 2 by nonbinary (0.7%) / 1 unknown
Note: I was pleasantly surprised by how many different books we got for this one; aside from the short story square, the only other square with more options was the "Fewer than 2500 Goodreads Ratings." When you have it wide open like this, you get a lot of choices, though still leaning male and "Fantasy popular."
2. Novel Featuring a Non-Western Setting
Books:
  1. (tie) Children of Blood and Bone by Tomi Adeyemi & The Three-Body Problem by Cixin Liu (13 times)
  2. Jade City by Fonda Lee (12)
  3. The Bear and the Nightingale by Katherine Arden & The Poppy War by R. F. Kuang (10)
TOTAL: 265 books (131 unique)
LEFT BLANK: 14 / SUBSTITUTED: 3
Authors:
  1. Cixin Liu (15 times)
  2. Katherine Arden (14)
  3. (tie) S. A. Chakraborty & Tomi Adeyemi (13)
TOTAL: 276 authors (103 unique)
GENDER: 147 by women (53.3%) / 122 by men (44.2%) / 4 by nonbinary (1.4%) / 3 unknown (1.1%)
Note: The first square that women "win," thanks to the popularity of 3 of the 4 most popular books.
3. Five Short Stories
Short Stories (all tied at 3 times):
  • “Fandom for Robots” by Vina Jie-Min Prasad
  • “I, Kane” by Laura M. Hughes
  • “In the Stacks” by Scott Lynch
  • “No Fairytale” by Ben Galley
  • “Welcome to Your Authentic Indian Experience” by Rebecca Roanhorse
TOTAL: 300 short stories (261 unique)
Authors:
  1. (tie) H. P. Lovecraft & Ken Liu (14 times)
  2. Neil Gaiman (9)
  3. (tie) Brandon Sanderson & Tanith Lee (7)
TOTAL: 304 authors (170 unique)
GENDER: 156 by men (51.3%) / 137 by women (45.1%) / 11 by nonbinary (3.6%)
Note: 60 people chose to read 5 short stories instead of reading an anthology but it was quite obviously with some of you that you were reading FROM a collection/anthology; why didn't you finish them?
Collections & Anthologies:
  1. (tie) Brief Cases by Jim Butcher; Lost Lore by Terrible Ten; & The Paper Menagerie and Other Stories by Ken Liu (9 times)
  2. (tie) Arcanum Unbounded by Brandon Sanderson & The Last Wish by Andrzej Sapkowski (6)
  3. The Language of Thorns by Leigh Bardugo (5)
TOTAL: 193 books (113 unique)
LEFT BLANK: 25 / SUBSTITUTED: 4
Authors:
  1. (tie) Andrzej Sapkowski & Jim Butcher (10 times)
  2. Ken Liu & Terrible Ten (9)
  3. Neil Gaiman (8)
TOTAL: 214 authors (105 unique)
GENDER: 122 by men (57%) / 80 by women (37.4%) / 1 nonbinary (0.5%)/ 11 unknown (5.1%)
Note: Not too many surprises for me, Ken Liu is a pretty popular short story writer, and Brief Cases came out last summer, and Sanderson and Sapkowski are subreddit faves.
4. Novel Adapted by Stage, Screen, or Game
Books:
  1. The Last Wish by Andrzej Sapkowski (11 times)
  2. The Princess Bride by William Goldman (10)
  3. (tie) A Wrinkle in Time by Madeleine L’Engle; Blood of Elves by Andrzej Sapkowski; The Last Unicorn by Peter S. Beagle; & The Magicians by Lev Grossman (8)
TOTAL: 260 books (120 unique)
LEFT BLANK: 18 / SUBSTITUTED: 4
Authors:
  1. Andrzej Sapkowski (25 times)
  2. Neil Gaiman (12)
  3. Terry Pratchett (11)
TOTAL: 269 authors (90 unique)
GENDER: 211 by men (78.4%) / 57 by women (21.2%) / 1 unknown
Note: This was the most male-dominated square on here, I think we can all guess why.
5. Hopeful Spec-Fic
Books:
  1. The Long Way to a Small, Angry Planet by Becky Chambers (12 times)
  2. (tie) The Curse of Chalion by Lois McMaster Bujold & Theft of Swords by Michael J. Sullivan (11)
  3. (tie) Nice Dragons Finish Last by Rachel Aaron & Sir Thomas the Hesitant and the Table of Less Valued Knights by Liam Perrin (8)
TOTAL: 260 books (151 unique)
LEFT BLANK: 19 / SUBSTITUTED: 3
Authors:
  1. Michael J. Sullivan (29 times)
  2. Becky Chambers (25)
  3. (tie) Rachel Aaron & Terry Pratchett (14)
TOTAL: 266 authors (113 unique)
GENDER: 152 by women (57.1%) / 112 by men (42.1%) / 2 unknown
Note: Even though NOT reading Chambers and Aaron would be hard mode, plenty of people wanted to read them anyway.
6. Fantasy Novel that Takes Place Entirely Within One City
Books:
  1. Foundryside by Robert Jackson Bennett (22 times)
  2. (tie) The Lies of Locke Lamora by Scott Lynch & The Thief Who Pulled on Trouble’s Braids by Michael McClung (16)
  3. Torn by Rowenna Miller
TOTAL: 253 books (132 unique)
LEFT BLANK: 18 / SUBSTITUTED: 11
Authors:
  1. Robert Jackson Bennett (24 times)
  2. Scott Lynch (17)
  3. Michael McClung (16)
TOTAL: 259 authors (115 unique)
GENDER: 165 by men (63.7%)/ 94 by women (36.3%)
7. Self Published Novel
Books:
  1. On the Shoulders of Titan by Andrew Rowe (8 times)
  2. Sufficiently Advanced Magic by Andrew Rowe (7)
  3. The Thief Who Pulled on Trouble’s Braids by Michael McClung (6)
  4. (tie) A Star-Reckoner’s Lot by Darrell Drake & Nice Dragons Finish Last by Rachel Aaron (5)
TOTAL: 250 books (168 unique)
LEFT BLANK: 21 / SUBSTITUTED: 11
Authors:
  1. Andrew Rowe (15 times)
  2. Krista D. Ball (11)
  3. (tie) Rachel Aaron & Will Wight (9)
  4. Phil Tucker (8)
  5. K. S. Villoso (7)
TOTAL: 250 authors (136 unique)
GENDER: 163 by men (65.2%) / 83 by women (33.2%) / 4 unknown
Note: I think most of the top authors here have a presence on the subreddit, but I'm definitely surprised that Rowe's books took BOTH top slots for this square.
8. Novel Published Before You Were Born
Books:
  1. A Wizard of Earthsea by Ursula K. Le Guin (10 times)
  2. The Forgotten Beasts of Eld by Patricia A. McKillip (7)
  3. (tie) Alanna: The First Adventure by Tamora Pierce; Dragonflight by Anne McCaffrey; & The Left Hand of Darkness by Ursula K. Le Guin (4)
TOTAL: 255 books (178 unique)
LEFT BLANK: 18 / SUBSTITUTED: 9
Authors:
  1. Ursula K. Le Guin (22 times)
  2. Patricia A. McKillip (9)
  3. Terry Pratchett (7)
  4. (tie) Anne McCaffrey; J. R. R. Tolkien; & Robert Jordan (6)
TOTAL: 262 authors (127 unique)
GENDER: 156 by men (59.5%) / 105 by women (40.1%) / 1 unknown
Note: Le Guin dominates this, as an easy recommendation for most of the younguns on the sub.
9. Any fantasy Goodreads Group Book of the Month
Books:
  1. All Systems Red by Martha Wells (54 times)
  2. Circe by Madeline Miller (17)
  3. Kings of the Wyld by Nicholas Eames (15)
  4. The Poppy War by R. F. Kuang (14)
  5. (tie) Foundryside by Robert Jackson Bennett & Trail of Lightning by Rebecca Roanhorse (12)
TOTAL: 262 books (59 unique)
LEFT BLANK: 18 / SUBSTITUTED: 2
Authors:
  1. Martha Wells (56)
  2. Madeline Miller (17)
  3. Nicholas Eames (15)
  4. (tie) R. F. Kuang & Robert Jackson Bennett (14)
  5. Rebecca Roanhorse (12)
TOTAL: 262 authors (53 unique)
GENDER: 161 by women (61.5%) / 96 by men (36.6%) / 5 by nonbinary (1.9%)
Note: Wells and Miller contribute to the women's domination of this category, with the overwhelming popularity of Murderbot quite evident. This is also a rather restrictive square, as there were only 68 books to choose from.
10. Novel Featuring a Library
Books:
  1. The Library at Mount Char by Scott Hawkins (40 times)
  2. The Invisible Library by Genevieve Cogman (31)
  3. Arm of the Sphinx by Josiah Bancroft (14)
  4. The Forbidden Library by Django Wexler (9)
TOTAL: 260 books (110 unique)
LEFT BLANK: 20 / SUBSTITUTED: 2
Authors:
  1. (tie) Genevieve Cogman & Scott Hawkins (40 times)
  2. Josiah Bancroft (15)
  3. Django Wexler (9)
TOTAL: 264 authors (96 unique)
GENDER: 150 by men (56.8%) / 113 by women (42.8%) / 1 unknown
Note: People love libraries and they love Cogman & Hawkins. Also, only 5 out of the 110 books had "Library" in their title... but 3 of them are in the top 4, hmm.
11. Subgenre: Historical Fantasy OR Alternate History
Books:
  1. His Majesty’s Dragon/Temeraire by Naomi Novik (11 times)
  2. The Bear and the Nightingale by Katherine Arden (9)
  3. (tie) A Star-Reckoner’s Lot by Darrell Drake & The Calculating Stars by Mary Robinette Kowal (8)
TOTAL: 267 books (153 unique)
LEFT BLANK: 13 / SUBSTITUTED: 2
Authors:
  1. Naomi Novik (20 times)
  2. Katherine Arden (19)
  3. Mary Robinette Kowal (10)
  4. Darrell Drake (9)
TOTAL: 269 authors (128 unique)
GENDER: 176 by women (65.4%) / 87 by men (32.3%) / 6 by nonbinary (2.2%)
Note: Another women-heavy square, I'm not surprised by any of the popular books or authors here.
12. Novel Published in 2018
Books:
  1. The Poppy War by R. F. Kuang (25 times)
  2. Children of Blood and Bone by Tomi Adeyemi (19)
  3. Grey Sister by Mark Lawrence (12)
  4. Trail of Lightning by Rebecca Roanhorse (10)
TOTAL: 269 books (130 unique)
LEFT BLANK: 12 / SUBSTITUTED: 1
Authors:
  1. R. F. Kuang (25 times)
  2. Tomi Adeyemi (19)
  3. Mark Lawrence (12)
  4. Rebecca Roanhorse (10)
TOTAL: 275 authors (133 unique)
GENDER: 140 by women (50.9%) / 134 by men (48.7%) / 1 unknown
Note: You're going to see Poppy War again and again.
13. Novel Featuring a Protagonist Who is a Writer, Artist or Musician (NOT: Kingkiller Chronicles)
Books:
  1. Bloody Rose by Nicholas Eames (14 times)
  2. Where the Waters Turn Black by Benedict Patrick
  3. (tie) Dust and Light by Carol Berg & Song of the Beast by Carol Berg (8)
TOTAL: 256 books (124 unique)
LEFT BLANK: 17 / SUBSTITUTED: 9
Authors:
  1. Carol Berg (17 times)
  2. (tie) Guy Gavriel Kay & Nicholas Eames (14)
  3. Brandon Sanderson (13)
  4. Benedict Patrick (10)
TOTAL: 260 authors (102 unique)
GENDER: 140 by women (53.8%) / 120 by men (46.2%)
Note: I'm highly amused that two different Berg books tied in this case. Also, even though the highest ranked book by Sanderson is only 31st overall, his general popularity means he may not always win a category but he's often around somewhere, especially with the 20 different squares he's used for.
14. Novel Featuring a Mountain Setting
Books:
  1. The Whitefire Crossing by Courtney Schafer (29 times)
  2. Spinning Silver by Naomi Novik (21)
  3. The Demons We See by Krista D. Ball (14)
  4. A Face Like Glass by Frances Hardinge (8)
TOTAL: 255 books (129 unique)
LEFT BLANK: 23 / SUBSTITUTED: 4
Authors:
  1. Courtney Schafer (29 times)
  2. Naomi Novik (21)
  3. Krista D. Ball (14)
  4. Mark Lawrence (11)
TOTAL: 257 authors (110 unique)
GENDER: 154 by women (59.9%) / 101 by men (39.3%) / 1 by nonbinary (0.4%) / 1 unknown
Note: If you read The Whitefire Crossing you read it for this square, no question. This was the most popular book only used for one square. Also, only 3 books have "Mountain" or "Mount" in them, and the highest ranked one is all the way down in 9th at 4 books.
15. 2017 fantasy Top Novels List
Books:
  1. The Traitor Baru Cormorant by Seth Dickinson (15 times)
  2. Red Sister by Mark Lawrence (10)
  3. The Fifth Season by N. K. Jemisin (8)
  4. Traitor’s Blade by Sebastien de Castell (7)
TOTAL: 263 books (127 unique)
LEFT BLANK: 16 / SUBSTITUTED: 3
Authors:
  1. Mark Lawrence (18 times)
  2. (tie) N. K. Jemisin & Seth Dickinson (16)
  3. Becky Chambers (12)
  4. Lois McMaster Bujold (11)
TOTAL: 273 books (64 unique)
GENDER: 209 by men (76.6%)/ 64 by women (23.4%)
Note: No real surprises here.
16. Novel with Fewer than 2500 Goodreads Ratings
Books:
  1. (tie) Sir Thomas the Hesitant and the Table of Less Valued Knights by Liam Perrin & They Mostly Come Out at Night by Benedict Patrick (5 times)
  2. (tie) Kings of Paradise by Richard Neull & The Empire of the Dead by Phil Tucker (4)
TOTAL: 265 books (225 unique)
LEFT BLANK: 16 / SUBSTITUTED: 1
Authors:
  1. Benedict Patrick (8 times)
  2. K. S. Villoso (7)
  3. (tie) Krista D. Ball; Liam Perrin; & Phil Tucker (5)
TOTAL: 272 authors (217 unique)
GENDER: 146 by men (53.7%) / 124 by women (45.6%) / 1 by nonbinary (0.4%) / 1 unknown
Note: Another one of my favorite squares for the sure number of unique books. Almost 80% of the cards have this square unique. If you look at the raw data, I recommend scrolling this section to see what might be new and interesting for you.
17. Novel with a One Word Title
Books:
  1. Touch by Claire North (11 times)
  2. Mort by Terry Pratchett (8)
  3. Worm by Wildbow (7)
  4. (tie) Borne by Jeff VanderMeer & Circe by Madeline Miller (5)
TOTAL: 267 books (183 unique)
LEFT BLANK: 13 / SUBSTITUTED: 2
Authors:
  1. (tie) Brandon Sanderson & Terry Pratchett (13 times)
  2. Claire North (11)
  3. Wildbow (9)
  4. Jeff VanderMeer (8)
TOTAL: 272 authors (149 unique)
GENDER: 166 by men (61%) / 106 by women (39%)
Note: The longest one-word title was Transformation by Carol Berg; the shortest was Ra by Sam Hughes. I think the longest one with one syllable is Scourged by Kevin Hearne. The shortest with multiple syllables is probably City (Simak) or Fyre (Sage) depending on you say that last one.
18. Novel Featuring a God as a Character
Books:
  1. Circe by Madeline Miller (26 times)
  2. Warbreaker by Brandon Sanderson (16)
  3. The Gospel of Loki by Joanne Harris (11)
  4. The Library at Mount Char by Scott Hawkins (8)
TOTAL: 267 books (119 unique)
LEFT BLANK: 14 / SUBSTITUTED: 1
Authors:
  1. Madeline Miller (30 times)
  2. Brandon Sanderson (19)
  3. Neil Gaiman (13)
  4. Brian McClellan (13)
TOTAL: 275 authors (88 unique)
GENDER: 151 by men (54.9%) / 124 by women (45.1%)
Note: Miller adds to her Circe lead with a bit of Song of Achilles.
19. Novel by an Author Writing Under a Pseudonym
Books:
  1. Assassin’s Apprentice by Robin Hobb (28 times)
  2. The First Fifteen Lives of Harry August by Claire North (11)
  3. The Goblin Emperor by Katherine Addison (9)
  4. (tie) 84K by Claire North & A Darker Shade of Magic by V. E. Schwab (8)
TOTAL: 259 books (136 unique)
LEFT BLANK: 19 / SUBSTITUTED: 4
Authors:
  1. Robin Hobb (58 times)
  2. Claire North (34)
  3. (tie) Ilona Andrews & James S. A. Corey (13)
TOTAL: 288 total (70 unique)
GENDER: 184 by women (63.9%) / 102 by men (35.4%) / 2 unknown
Note: Raise your hand if you were surprised by this AT ALL, and I still wouldn't believe you.
20. Subgenre: Space Opera
Books:
  1. The Long Way to a Small, Angry Planet by Becky Chambers (20 times)
  2. Space Opera by Catherynne M. Valente (19)
  3. Leviathan Wakes by James S. A. Corey (9)
  4. (tie) Ancillary Justice by Ann Leckie & Record of a Spaceborn Few by Becky Chambers (7)
TOTAL: 258 books (118 unique)
LEFT BLANK: 21 / SUBSTITUTED: 3
Authors:
  1. Becky Chambers (27 times)
  2. Catherynne M. Valente (19)
  3. James S. A. Corey (17)
  4. John Scalzi (13)
TOTAL: 278 authors (85 unique)
GENDER: 154 by men (55.4%) / 118 by women (42.4%) / 6 by nonbinary (2.2%)
Note: I'm disappointed in you all for not getting the actual book called Space Opera to the top.
21. Stand Alone Fantasy Novel
Books:
  1. The Goblin Emperor by Katherine Addison (9 times)
  2. The Night Circus (8)
  3. Uprooted (7)
  4. (tie) Balam, Spring by Travis M. Riddle; Spinning Silver by Naomi Novik; & Tigana by Guy Gavriel Kay (6)
TOTAL: 268 books (171 unique)
LEFT BLANK: 13 / SUBSTITUTED: 1
Authors:
  1. Naomi Novik (13)
  2. Guy Gavriel Kay (12)
  3. Neil Gaiman (11)
  4. Katherine Addison (9)
TOTAL: 278 books (151 unique)
GENDER: 146 by men (52.5%) / 132 by women (47.5%)
Note: It's interesting to see Riddle's book make it so high here compared to the general popularity/recommendations of the others mentioned here.
22. Novel by a RRAWR Author OR Keeping Up With the Classics
Books:
  1. Senlin Ascends by Josiah Bancroft (20 times) [RRAWR]
  2. Alanna: The First Adventure by Tamora Pierce (18) [Classics]
  3. The Princess Bride by William Goldman (17) [Classics]
TOTAL: 246 books (47 unique)
LEFT BLANK: 24 / SUBSTITUTED: 12
Authors:
  1. Josiah Bancroft (23 times)
  2. Tamora Pierce (18)
  3. William Goldman (17)
TOTAL: 250 authors (43 unique)
GENDER: 176 by men (70.4%) / 74 by women (29.6%)
Note: I'm pleasantly surprised that the divide between the two clubs here is almost even: 125 books (25 unique) for RRAWR / 121 books (22 unique) for Classics. This was always going to be a tough square because of the limited number of books (only 24 in the end for Classics, and only about 24 authors for RRAWR [now RAB]).
23. Novel from the fantasy LGBTQ+ Database
Books:
  1. (tie) On the Shoulders of Titans by Andrew Rowe & Sorcerous Rivalry by Kayleigh Nicol (10 times)
  2. The Gentleman's Guide to Vice and Virtue by Mackenzi Lee (9)
  3. Sufficiently Advanced Magic by Andrew Rowe (8)
TOTAL: 270 books (143 unique)
LEFT BLANK: 12 / SUBSTITUTED: 0
Authors:
  1. Andrew Rowe (18)
  2. (tie) Kayleigh Nicol & Nicholas Eames (11)
  3. (tie) Mackenzi Lee & Mark Lawrence (10)
TOTAL: 281 authors (130 unique)
GENDER: 158 by women (56.2%) / 112 by men (39.9%) / 11 by nonbinary (3.9%)
Note: I'm glad to see that people took the challenge!
24. Format: Graphic Novel (at least 1 vol.) OR Audiobook
Graphic Novels:
  1. Monstress by Marjorie Liu (19 times)
  2. Saga by Brian K. Vaughan (9)
  3. (tie) White Sand by Brandon Sanderson & Rik Hoskin & Nimona by Noelle Stevenson (7)
TOTAL: 171 graphic novels (109 unique)
LEFT BLANK: 20 / SUBSTITUTED: 6 [shared with Audiobooks]
Authors:
  1. Marjorie Liu (19 times)
  2. Brian K. Vaughan (12)
  3. Noelle Stevenson (9)
TOTAL: 200 authors (111 unique)
GENDER: 138 by men (69%) / 62 by women (31%)
Note: I actually tried to convince lrich1024 to make the hard mode this year both Saga AND Monstress, so I'm not surprised Monstress had a good showing here!
Audiobooks: All tied at 2 each:
  • Midnight Riot/Rivers of London by Ben Aaronovitch
  • Red Rising by Pierce Brown
  • Ship of Magic by Robin Hobb
  • Storm Front by Jim Butcher
Authors: All tied at 3 each
  • Ben Aaronovitch
  • Brandon Sanderson
  • Jim Butcher
  • Robin Hobb
TOTAL: 88 authors (76 unique)
GENDER: 62 by men / 26 by women
LEFT BLANK: 20 / SUBSTITUTED: 6 [shared with Graphic Novels]
Another crazy square in which no one really dominates because of the lack of restrictions otherwise.
25. Novel Featuring the Fae
Books:
  1. The Cruel Prince by Holly Black (11 times)
  2. Fae: The Wild Hunt by Graham Austin-King (9)
  3. (tie) Rosemary and Rue by Seanan McGuire & Stardust by Neil Gaiman (8)
TOTAL: 262 books (142 unique)
LEFT BLANK: 16 / SUBSTITUTED: 4
Authors:
  1. Sarah J. Maas (25)
  2. Holly Black (21)
  3. (tie) Jim Butcher & Seanan McGuire (15)
TOTAL: 263 authors (101 unique)
GENDER: 158 by women (60%) / 105 by men (40%)
Substitutions
Out of 282 cards, 102 used the Substitution rule.
Books: No books were used as substitutes more than once except for the following 4 books: Into the Drowning Deep by Mira Grant; Just One Damned Thing After Another by Jodi Taylor; The Blade Itself by Joe Abercrombie; & The Fifth Season by N. K. Jemisin.
Squares: 36 squares from past Bingos were used as substitutes with the most popular being:
  1. (tie) Dystopian / Post-Apocalyptic / Apocalyptic / Dying Earth (from 2017) & Sequel: Not the First Book in the Series (from 2017) (8 times)
  2. (tie) Non-fiction Fantasy Related Book (from 2017) & Science Fantasy OR Sci-Fi (from 2016) (7)
Just One Damned Thing After Another was the only one used for the Time Travel substitute, which happened twice.
Of the 102 substituted books, 54 were by women (52.9%)
Note: Someone apparently would rather read a 1000+-page book by Brandon Sanderson (Oathbringer) than read Five Short Stories. I can't stop laughing at this.
Also, another bingo participant decided to replace the Hopeful Spec-Fic square with Dystopian / Post-Apocalyptic / Apocalyptic / Dying Earth. Who hurt you?

PART II: The People You Know and Love

In addition to the popularity charts above, I also ran through each individual card to figure out a few things:
  1. How much of your card did you submit (a full 25, or less than that?)
  2. How many squares had women/non-binary people in them?
  3. What was the unique title count? As in, how much of what you read was unique to your card?
  4. How many people have done the Bingo more than once?
  5. NEW: How did Hard Mode go this year?
Card Completion
282 cards were submitted by 264 people. Of the multiple-card submitters, 16 turned in 2 cards and two turned in 3 (among the secondary cards, 3 were incomplete).
47 out of 282 cards (16.7%) did not fill out all 25 squares. Each submitted card had at least 5 squares filled. In 2017, 44 out 243 cards (18%) weren't fully filled out.
One person had cards with only 24 squares submitted. Ouch! Better luck next year. :)
Gender in Cards
I counted a card as having a woman/non-binary person on it if at least one woman/non-binary person was involved. So if you read an anthology that had at least one story by a woman, it counts. If you submitted 5 short stories and one was by a woman, it counts.
6 out of 282 cards (2%) had zero men on them (with one incomplete card having all 18 squares by women/nonbinary). 16 other cards had at least 20 women.
There was an average of 11.4 women/nonbinary across all cards. The average raises to 12.2 for complete cards. This differs only slightly from 2016's 12.3 average for complete cards.
Two cards had zero women/nonbinary on them (both were 5-square-only cards). Among the 235 completed cards, two of them had only 1 woman/nonbinary on them
Unique Title Count
I specifically did not count short stories submitted, but did count anthologies and collections. (There were 300 short stories submitted and they had a very high unique rate overall).
For 2018, the average number of unique titles per card was 5.2. Three cards had 0 unique titles (everything they read was read by someone else). 8 cards had at least 12 unique titles, with only one person at 15 unique titles. As more people join Bingo, it becomes harder to get those unique titles.
(For 2017, the average number of unique titles per card was 5.3. Ten cards had 0 unique titles. 17 cards had at least 12 unique titles, with only one person at 17 unique titles. In 2016, the average unique count was 6.8, and no cards had 0. 11 cards had at least 12, with one person at 15. In 2015, the average unique count 8.0, and no cards had 0. 18 cards had at least 12, with one person at 18.)
Repeat Bingo Readers
From the survey we included int he Google Form, 31 of the 264 of you (11.7%) have done Bingo each year since 2015. Well done you!
Amazingly 113 say this is your first time doing Bingo--that's 42.8%! Wow.
NEW: Hard Mode
30 out of 282 cards were 100% hard mode cards. Another 7 just missed it by one square. 9 people didn’t bother with hard mode at all, including 6 complete cards. Average hard mode count was 11 squares, 12.3 for complete cards.
EDIT: Thanks to mantrasong for the calculating the following:
Fewest Hard Mode entries:
  1. Novel by a RRAWR Author OR Keeping Up With the Classics (61/246 - 24.8%)
  2. Any fantasy Goodreads Group Book of the Month (69/262 - 26.34%)
  3. Novel Published Before You Were Born (73/255 - 28.63%)
  4. Novel from the fantasy LGBTQ+ Database (78/270 - 28.63%)
  5. Novel Featuring a Non-Western Setting (78/265 - 29.85%)
Most Hard Mode Entries:
  1. Novel Featuring a Library (167/260 - 64.23%)
  2. Format: Graphic Novel (at least 1 vol.) OR Audiobook (174/256 - 67.97%)
  3. Stand Alone Fantasy Novel (176/268 - 65.67%)
  4. Five Short Stories (193/253 - 76.28%)
  5. Hopeful Spec-Fic (195/260 - 75%)

PART III: Measuring Variety

Something I've been interested in for the last couple years is trying to figure out how to meaningfully measure the overall variety of selections per square. For example, in the 2015 bingo, in the Comic Fantasy square, Terry Pratchett was read for 42 of the 88 cards. The next most popular author had only 5 reads. That's quite lopsided!!!
In the end, I decided to try to use the Gini index. The Gini coefficient is used by economists to measure income inequality, where 0 = everyone has the same income to 1 (or 100 in my case) = the income is concentrated in one individual.
In our case, instead of income, I'm using the number of books read and authors read. If, for example, 25 different books are each read once, its "FarraGini" index would be 0 (all books were read equally). If 24 books were read once and the 25th book was read 51 times, its FarraGini index would be 64. So the more widely spread a category is read, the lower its index number.
I've created a table below of all the categories (splitting short stories into individual Stories & Collections, and Graphic Novel and Audio) and their FarraGini indices per book and author.
You'll notice that the FarraGini index for Goodreads Group Book of the Month has the highest single number for book as All Systems Red dominated its category, but also that Pseudonym has the highest FarraGini index for author, since Robin Hobb accounts for 20% of all books in that category.
CATEGORY BOOK AUTHOR
01. Novel that was Reviewed on Fantasy 19.7 30.7
02. Novel Featuring a Non-Western Setting 41.8 49.4
03SS. Five Short Stories (Short Stories) 11.5 35.3
03CA. Five Short Stories (Collections/Anthologies) 34.1 40.4
04. Novel Adapted by Stage, Screen, or Game 40.3 47.6
05. Hopeful Spec-Fic 36.6 49.7
06. Fantasy Novel that Takes Place Entirely Within One City 41.6 47.0
07. Self Published Novel 27.4 38.5
08. Novel Published Before You Were Born 24.4 39.4
09. Any fantasy Goodreads Group Book of the Month 56.9 56.4
10. Novel Featuring a Library 51.1 55.6
11. Subgenre: Historical Fantasy OR Alternate History 35.6 44.1
12. Novel Published in 2018 44.1 43.9
13. Novel Featuring a Protagonist Who is a Writer, Artist or Musician (NOT: Kingkiller Chronicles) 39.6 47.7
14. Novel Featuring a Mountain Setting 44.0 49.5
15. 2017 fantasy Top Novels List 39.1 45.1
16. Novel with Fewer than 2500 Goodreads Ratings 13.8 18.5
17. Novel with a One Word Title 27.0 37.9
18. Novel Featuring a God as a Character 44.7 52.9
19. Novel by an Author Writing Under a Pseudonym 41.5 62.7
20. Subgenre: Space Opera 43.5 55.0
21. Stand Alone Fantasy Novel 31.2 39.3
22. Novel by a RRAWR Author OR Keeping Up With the Classics 47.1 47.2
23. Novel from the fantasy LGBTQ+ Database 38.9 43.7
24G. Format: Graphic Novel 33.1 38.9
24A. Format: Audiobook 4.5 12.4
25. Novel Featuring the Fae 37.2 50.8
Overall 52.4 66.7
As you can see above, the numbers paint a picture that we've seen in the individual square sections above--the FarraGini indices for Reviewed and <2500 Goodreads ratings are pretty low because of the variety (with Audiobooks at an insane number), where Goodreads Book of the Month and Pseudonym indicate that a book or author is really weighting numbers towards it.
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2019 Offseason Review Series: Day 18 - The Carolina Panthers

Team: The Carolina Panthers

Division: The NFC South

It’s that time of year again! After a season that could best be described as “a hangover you don’t deserve”, we watched the Panthers soar to a 6-2 record. After a beatdown of eventual playoff caliber Baltimore, It finally looked like we were poised to shrug off our non-consecutive winning streak habit. But it was not meant to be. A combination of shallow defensive depth and a lingering shoulder issue for Cam Newton saw us collapse down the stretch, and we ended 7-9 winning only a single game. After watching the sharp downturn of our fortunes, questions surrounding our QB’s health and a major exodus of our most tenured veteran talent, one could be forgiven for a glum outlook on the franchise’s future going into this offseason.
But despite the spirit in which we entered it, this offseason has been a resounding success. And one that leaves little doubt that we’re an improved team despite our more prominent losses. What follows is a point for point breakdown in how we made the transition from collapsed contender to potential comeback story.

Coaching Changes

None whatsoever.
From both the commentator sphere and other fanbases, the Panthers were pretty roundly rebuked for hiring offensive coordinator Norv Turner. Despite alarms being raised over 7 step drops and an over reliance on deep shot, Turner was a revelation for our offense. He apparently meant every word of emphasizing high completion throws and taking pressure off of Cam, and we began to see looks for our QB that were totally absent in the Mike Shula era. He’s now had a chance to throw dump offs, and to have reliable comeback options. Cam, prior to breaking down, was enjoying one of the best seasons of his career and despite the shoulder injury, still finished with a career high completion percentage. Christian McCaffery, our other offensive mainstay, saw his rushing efficiency go from 3.7 YPC his rookie season to 5.0 yards in year two, with his total scrimmage yardage upticking from 1,086 to 1,965 in Norv’s new passing and blocking system. Turner’s tenure thus far has been an unmitigated success and a refreshing change of pace from the stale, dull system we fell into under Shula.
The other transition, from Steve Wilkes to Eric Washington at defensive coordinator, yielded decidedly more mixed results. Washington, simply put, was not good in his transition from the DL coach. In over his depth. He struggled all year, culminating in Rivera assuming defensive playcalling down the stretch. The turnaround in our defense once he did was remarkable, though by that point, Cam was falling apart so visibly that what happened on that side of the ball no longer mattered. Washington has been retained for the upcoming season, but Rivera’s going to keep the playcalling duties.
And captaining the ship is Rivera himself. Despite a call for his head among our fanbase’s more frustrated elements, Rivera was kept for 2019. And I’m glad for it. All or Nothing (though I’ve not had a chance to see it) provided a window into his management style, vindicating some like me who pushed back against narratives that he was a dispassionate robot. And while I’m a bit higher on Ron than many, I don’t think it’s unsafe at all to say that none of the coaching hires would have represented an obvious upgrade. At the end of the day, Rivera lead a squad to 6-2 before his QB’s season derailed, which is not really on him. He could maybe be criticized for letting Washington fail for too long, but at the end of the day, few of our woes from last year can be solely attributed to him. While this is certainly a put up or get out year for Rivera, I have little doubt that he’ll be leading the gang come 2020 as well.

Departures

Thomas Davis, LB - Now we get into the stuff that hurts. And this one really, really hurts. I understand it. We needed to figure out whether Thompson could stand on his own like, yesterday so we can decide his long term potential. Davis, while still playing at a high level, is an old man for the position he plays. Letting him walk was a logical decision. But none of it changes the fact that Davis has been the soul of this defense for over a decade, and was easily one of the most beloved players and leaders over the 14 years he spent with us. He will be missed, both for his play and his spirit.
Julius Peppers, DE - Speaking of franchise staples, long time DE and future Hall of Fame inductee Julius Peppers’ watch has ended. Unlike Davis, who we simply allowed to leave, Pep has called it a career. And what a career it was. Though almost every single article about our defensive adjustments leads off with “With Peppers retiring, the Panthers no longer have anyone who can rush the passer”, the reality is that Pep did far less than his opposite in Mario Addison to that effect. Though he came back to us in 2017 with a monster 11 sack season, that number was always misleading given how few pressures he accomplished it on. Last year, he came back down to earth. It was time, and while I wish we could have given Pep one last, Super Bowl winning hurrah, a new direction was needed.
Ryan Kalil, C - Ryan Kalil rounds out our list of beloved departing veterans. The anchor of our offensive line for 12 years has hung up his cleats. Of all the offseason changes, this was by far the scariest, as the difference between Cam with and without a good center of the course of his career has been stark and terrifying. Kalil was a damn good player right up to the end, though the rash of injuries he suffered between 2016 and 2018 clearly took their toll on his performance. And while we have replaced him (and debatably upgraded), Kalil was both a locker room leader and a damn good contributor that will be missed by all.
Devin Funchess, WR - We now get into the departures who will be less missed. Funchess, admittedly, gets a bit of a bad wrap from our fanbase who often talk about him as though he were trash. While not trash, he is at least very replaceable. In fact, Funchess replacement began well before the expiration of his contract, as he had been fully supplanted by rookie DJ Moore and sophomore Curtis Samuel down the stretch last year. By the end, he was a healthy scratch. While I’m sure he’s going to put up numbers in Andrew Luck’s offense, Funchess is no sort of elite talent. He’s a big body who fails to gain separation and who inconsistently leverages his size to his advantage. I view his upside as a Brandon LaFell type of guy. And that type of guy is no longer a fit for what we’re trying to do.
Matt Kalil, OT - If the Carolina fandom is ambivalent about Funyun’s departure, we’re positively giddy about this one. Cut with a June 1st designation, Kalil saved us the money that allowed other moves to be possible. Though the shine has come off the diamond that was Gettleman’s tenure with us, the man often doesn’t get the credit he should. He did do a great deal for us, particularly his completely unheralded building of our OL (No less than 3 of our 5 starters this coming season will have been Gettleman acquisitions). But by far the biggest mistake in his tenure was the massive albatros of a contract he doled out to Matt Kalil, who could not have failed more spectacularly (or predictably) to live up to it.
Mike Adams, FS - I speak on behalf of the fanbase when I say that we have nothing but respect for Adams. He was a solid player and a veteran leader who spent his last two years giving lift to a secondary that hasn’t seen a great safety tandem since the Clinton Administration. But your eyes don’t deceive. We really were running his 37 year old ass out there as a free safety. And that simply could not be allowed to continue. I wish Adams the best, but it was time to move on.

Arrivals

Matt Paradis, C - Here’s the fun stuff. After losing Kalil to retirement, we signed former Broncos safety Matt Paradis to replace him. At only 29, Paradis represents a significant youthening at the position, and for a guy whose upside is top 5 at the position, we got him at a significant discount. Obviously that discount was due to medical risks, which prompted his release by the Broncos in the first place. But Paradis’ has been fully cleared from day 1 and avoided the PUP list. By all accounts, he’s in tip top shape. We’ll obviously see how that holds up as the season gets underway, but Paradis is definitely one of the steals of the 2019 free agency period and I could not be happier to have him. His arrival is enormous for our prospects, and has turned our biggest positional question mark into an area of strength.
Daryl Williams, OT - It’s a bit disingenuous to call Williams an arrival, as he never actually left. But that he never left is nothing short of remarkable. After a 2017 All Pro season, Williams suffered a major setback of an injury in 2018 training camp that eventually turned into a season ending injury after he tried to rush back. Still though, the League is constantly hungry for All Pro level OT talent and I was sure Williams was going to get scooped up. Instead, he signed a 1 year, $6 million deal to come back to us, and short of black magic I’m not entirely sure how Marty Hurney pulled it off. Williams is a terrific player who can play many parts of the OL. He can slot in at LG if rookie OT Greg Little can win the LT job, but also provides insurance at LT if he can’t. He and Moton playing opposite one another represents the best OT tandem that Cam Newton has ever enjoyed.
Gerald McCoy, DT - Awwwww yeah! My all time favorite Tampa Bay Buccaneer is now a Carolina Panther. McCoy is a rock solid DT who truly needs no introduction from me. How we plan to use him is a bit murkier, but use him we definitely will. I suspect to see McCoy playing DT opposite Kawaan Short in our 3-4 looks (more on that in a minute), to line up next to him in our 5-2 looks, and to work with him on pass rushing 4-3 sets. He adds more juice to a pass rush that already saw a healthy injection of talent this year, and is more consistent in the run game than some of the other DL on the roster, which was a notable area of weakness last season. He fits the versatility first mold that’s going to allow Rivera to mix up our defensive looks as transition fully to a hybrid, and is a terrific leader in the locker room besides. Our beat writers have described him as “joined at the hip” with Kawaan Short, and I fully expect the pair to make one another better.
Bruce Irvin, OLB - Perhaps the first real signal that this wasn’t going to be the Carolina defense of yesteryear, Irvin is a vet leadership, change of pace signing. In moving to a hybrid defense, we acquired a number of rookie talents to complement OLBs like Marquis Hayes. Irvin rounds out that group, and provides us with a valuable cog in pass rushing sets and a good leader for the younguns. Though he’s not as disruptive as he once was, Irvin is a rock solid player who provides us with quality depth and leadership.
Chris Hogan, WR - A graduate of the Patriots Random White Guy Academy, Hogan flashed serious potential for his first couple of years in New England before getting gradually phased out of the offense. I’m not expecting much, but he has the potential to help us on deep balls and it’s generally never a bad thing to have more talent at WR.
Aldrick Robinson, WR - Robinson does one thing and one thing only, which is catch touchdowns. Conveniently, that’s one thing we struggled with last season. But with Greg Olsen now fully healthy and a sudden wealth of other options at WR, I would give Robinson long odds of making the roster.

Draft

Pick 1.16: Brian Burns, DE/OLB - I am still in shock that Brian Burns was available at pick #16. I wanted him very badly, but I was certain he’d be an Atlanta Falcon. Instead, people allowed him to fall all the way to us and I couldn’t be happier. Burns is the apotheosis of what we’re trying to accomplish with our defensive transition. He’s a guy as comfortable upright as he is with his hand in the dirt. While he lacks strength as a run defender, he has incredible burst off the edge and a ludicrously high ceiling as a pass rusher. I think he landed on a terrific team to turn that potential into reality and I’m extremely excited about what he can do with us.
Pick 2.37 Greg Little, OT - Every description I’ve ever read of Little has described him as “Pro Ready”, and the team clearly drafted him with an eye on starting at LT. Luckily, we’ve hedged that bet a bit with the Daryl Williams signing, but Little still projects as a talented young player with a high floor and a well rounded skillset. If not the LT starter this year, he’ll almost certainly have the job to himself next season.
PIck 3.100 Will Grier, QB - Boy did this piss people off at the time. Though cooler heads have since prevailed, this pick was seen by one group of reactionaries as an indictment on Cam’s health, and another as a wasted pick on a player who will never produce for us. The reality is neither. While Cam’s health is in good shape (put a pin it), we were put in a position last year in which he needed to rest a clearly deteriorating shoulder, but we had no faith in the men behind him to win games. If that’s the state of your backup, you need a better backup. This is a team that has seen playoff runs hinge on a game or two that Derek Anderson filled in for. So even as high as pick 100, Grier was a worthy investment. In terms of his playstyle, Grier slots as an accurate QB with a good deep ball and a cerebral style, but average arm strength and mediocre release.
Pick 4.115 Christian Miller, OLB - Like Burns, Miller projects as a do-all DE/OLB who can play either upright or down low. He’s an athletic prospect whose game is a bit raw, but who checks all the measurable boxes. Likely a top 50 player before injuries kept him out of the pre-draft process, Miller represents a hell of a value at 115. I suspect we’ll see he and Burns as long term staples of the pass rush.
Pick 5.114 Jordan Scarlett, RB - This was a bit of an odd one, but I’ve warmed to it over time. Scarlett is a bruising, violent running back who I’m almost certain was drafted to lend a hand in the red zone. As a change of pace to CMC, the two could not be more different. But coaches thus far have raved about his conditioning and power, so the pick may not have been as crazy as it looked at the time. Having said that, while I don’t think anyone should ever get upset over a 5th round pick, I do think we could have found better value at this position. Scarlett wasn’t likely to be gone by the time we selected our next player.
Pick 6.212 Denis Daley, OT - I like this pick quite a bit. Daley had a rough statline in terms of sacks allowed when facing a veritable who’s who of elite college pass rushers (Jachari Polite, Josh Allan, Clelin Ferrell among them). But in spite of that, scouting reports consistently cite both his physical gifts and his improvement as the season went on. If he can cut down on his most egregious habits (most notably his overeager lunging at edge rushers), he has legit starting potential.
Pick 7.237 Terry Godwin, WR - Godwin’s whole game is predicated on speed and football IQ. At 5’11, it’s certainly not coming from his physical measurables. But he was by all accounts a high work ethic, smart players who contributed admirably in his four years as Georgia starter. Godwin’s ceiling is likely a Curtis Samuel backup, but his early rapport with Cam makes me think he’ll stick on the roster despite his late draft spot.

Strengths and Weaknesses

Offense - With Cam’s health reportedly looking good (particularly his ability to throw deep; something he was never capable of throughout Camp) and the team adapting so well to Norv Turner’s system, I think offense as a whole is a good place to start. Though I said it last year, only to be hilariously wrong, Greg Olsen is operating at 100% as well, which provides a boost to our red zone effectiveness that is difficult to measure. By the end of last year, both DJ Moore and Curtis Samuel appeared to be on the cusp of a major breakout, both proving themselves so reliable that Devin Funchess was a healthy scratch by week 17. Those two should continue to grow, and Jarius Wright has proven to be a valuable slot receiver. And, of course, there’s CMC, who will continue to be our best offensive weapon not named Cam Newton. With good health and plenty of diverse options, I suspect the good times to continue to roll as we enter year two of Turner’s stewardship.
Offensive Line - I can’t emphasize this enough, but our offensive line is nasty. With Williams’ return, we now have an All Pro OT to pair with breakout sensation Taylor Moton, which makes for an excellent tandem. Matt Paradis replaces, and if we’re being honest, provides an upgrade over Ryan Kalil, and Trai Turner is as effective a RG as ever. LG will likely be manned by whichever of Williams or Little doesn’t win LT, and Greg Van Roten (who’s performed admirably at the position) is still in the building as well. This is a very solid group of players, and a massive upgrade over what we had to work with last year.
Pass Rush - This was a major area of concern last year, but I’m happy with where we’re at now. The transition to a hybrid defense was the right call for our personnel set, and between the draft and free agency, we’ve upgraded across the board. McCoy is a huge boost to our interior pressure and Brian Burns should contribute immediately. Efe Obada will likely continue to grow, and the new system is a much better fit for talented sophomore Marquis Hayes. Irvin is solid rotational addition as well, and Mario Addison is as stalwart a pass rusher as ever. All in all, we’ve gone from an extremely one dimensional pass rush to one that is versatile and capable of throwing multiple looks at our opponents. We will be hard to predict and hard to stop when we come at the QB next year.
Weaknesses
Run Defense - Though I’ve seen little attention paid to it, I’m very concerned about our run defense this year. Although we’ve beefed the hell out of the defensive front, few of these pieces excel in run defense. McCoy has mostly staked his reputation on being a 3 tech. Hayes, Miller and Burns were all flagged as prospect that lacked run support talent. Poe was miserable in defending the run last year, and it’s never really been Short’s bag. In terms of yards per carry, we finished 8th overall which sounds good. But this was mostly on the strength of changes when Rivera took over the playcalling, as backs tended to run over us consistently early in the year. As long as we have Luke, our run defense will be solid. But I do worry that with so much (needed, mind you) emphasis put on rushing the passer, we’ve left off this part of the game.
The Secondary: As always with us, the secondary is a concern. It is, to be fair, less a concern than in previous years. Donte Jackson and James Bradberry both enjoyed very solid campaigns last year, and the former has allegedly done a lot of growing over the previous season. Eric Reid represents a good, solid strong safety. But free safety is, as ever, a mess. The job is going to sophomore player Rashaan Gaulden, but I think his capturing the position unopposed has less to do with what coaches see in him, and running out of money after doling out contracts to Paradis, McCoy and Williams. Our secondary, while improved, was inconsistent last season and was the primary reason we finished in the middle of the pack.
And honestly, that’s about it. This is one of the strongest rosters Carolina has fielded in the Riv-Era, at least on paper.

X Factors

Cam’s Health - Those of your who frequent nfl have likely seen my refrain on this many a time, but Cam’s health is not as dire as last season made it look, and the Andrew Luck comparisons have always been, frankly, crazy. In 2016, Cam tore his rotator cuff. He rushed his recovery in order to play in 2017. This created a buildup of scar tissue which, when coupled with a minor bone spur, caused a great deal of swelling this year that put Netwon in pain and limited his range of motion. It’s one of those injuries that, while not terrible by any means, does require either surgery or a great deal of rest. Cam, by virtue of being alpha and omega to this team, had the luxury of neither. The swelling persisted until he could barely throw. While that looks scary, the actual diagnosis was not that grim, and a simple shoulder scope as cleared the damage. By all accounts, he’s 100% and even making throws that he was incapable of these last two years. Bill Voth, who was the first (and for a long time, only) writer sounding the alarm on Cam’s strength as far back as 2017, has said that he’s making throws that look like his old self routinely.
However, we are putting him on a pitch count. This like likely vet maintenance rather than a source of genuine alarm. But after the last couple of years, he does make you sweat a little.
OL Health - The major fly in the ointment when it comes to Carolina’s optimism over its OL is that big if healthy caveat. If healthy, Paradis is a top 5 Center. If healthy, Williams has All Pro talent. 4 days into camp, however, neither is participating in serious pass rush drills and only today suited up in pads. It is possible that they’re just being eased along. They did avoid the PUP list, which we were almost sure was going to get Paradis at the very least. So they appear to be alright. But if they’re not, or they reinjure again, we go from being an extremely strong team to a fatally flawed one. A great deal is riding on the health of those two players, and the entire house of cards could fall apart quickly if they’re unable to deliver.
Greg Olsen - The one health flag that I do have complete confidence in is tight end Greg Olsen. Suffering a series of foot breaks, he is now moving around at 100% capacity and has been medically cleared for all activity for months. Bone breaks are, when all is written, temporary injuries that often heal stronger when they actually get a chance to heal. Our most trusted beat writers, Voth and Rodrigue, have both been crystal clear that he looks like his old self and that his connection with Newton is as faithful as ever. What I’m less clear on is his role in the offense. For years, Greg Olsen was the pivotal piece of our passing game. But with his largely being sidelined with foot injuries over the last two years, the game has moved on. Curtis Samuel and DJ Moore are both going to receive plenty of targets, and McCaffery will be a critical element to the passing game. Greg will undoubtedly be our principle red zone threat, but the growth of other options has downgraded his loss from catastrophic to merely unfortunate. What role he carves out, and what boost he’s able to give our offense, will be very interesting to watch.
4-3 No More: Much has been made of the Carolina's transition from a 4-3 to a 3-4 this offseason. And most of it is crap. We aren't exactly moving in a direction that binary. IN the past, we have strictly been a 4-3 team throughout the Riv-Era. That is about to change, but not to a 3-4. What Rivera showed last year is a willingness to mix and match personnel sets. There were 3-4 looks, 4-3 looks and even 5-2 looks. What we're moving toward is thus not a single, codified base, but a hybrid defense that can throw out a number of formations and switch between them quickly. We want players who can play OLB and DE. DTs who can play DE. LBs who can drop into coverage and rush the passer. A modern defense is one that doesn't limit itself, which is why such a premium has been put on players with positional versatility. On paper, our personnel set is very well built for this. How it pans out in practice remains to be seen. It's a very radical transitioning happening over a short period of time, and while I think our defense has the potential to be excellent, there will doubtless be some growing pains as we navigate the transition.

Positional Battles

Very little to speak of. The premier battle is going to be between Greg Little and Daryl Williams at LT. Apart from that, the timeshare that forms in different defensive sets will be intriguing. But for the most part, the roster is set.

Win Loss Predictions

I hate this part, particularly since the NFCS is a murderers row at present. The Panthers have a shot at a serious playoff run if all the chips fall right, but the Falcons are likely going to be resurgent (god you have no idea how much it hurts me to type that) and the Saints aren’t going anywhere. The Buccs I’m sure will do their best.
That alone makes pinpointing what our season looks like in terms of Ws and Ls difficult. But this year, we’re also playing the equally enigmatic AFCS, whose teams look like contenders or middlers in turns. Even our other divisional draw, the NFCW, is difficult to find the pulse of.
So rather than pretend that I know what each game is going to look like, I’m going to do what I always do; Likely wins, likely losses, toss ups.
Likely Wins: TB, @AZ, JAX, @TB, @SF, TEN, WAS
Likely Losses: LAR, @NO, @IND
Toss Ups: @HOU, @GB, ATL, NO, @ATL, SEA
So that’s 7 likely wins, 3 likely losses and 6 toss ups.
If that seems like an unusually high degree of uncertainty, that’s because it is. Last year started off strong and fell apart for reasons that are both obvious and cautiously behind us. We’ve only improved over the offseason and should be formidable. But the schedule is grueling and many questions are yet unanswered. I said in my last offseason review that last year was likely going to be a tough season, and should be viewed mainly as a proof of concept for the new ideas we were incorporating via Turner’s offense and our gradual move away from a 4-3 defense. Well, it was a tough year for reasons of which I had no inkling at the time, and it was a proof of concept. And for the most part? The concept was proven sound. So this offseason, we’ve built on it and patched over the holes that developed in it.
I know that “This offseason is a major turning point” is one of those things that gets thrown around a lot. It’s like how every Presidential election gets described as historic, as though choosing the leader of the free world could ever be anything but. But in a very real sense, this franchise has hit a turning point. Cam has to bounce back this year or he’ll face major doubts about his future contract. Rivera has to bounce back this year, or he’ll be out of a job. GM Marty Hurney has done an excellent job restocking the cupboards, but we’ve been down this road of defensive transition and an offense that eases things on the quarterback before. Last year, both ideas mostly worked, but this is the season where we must commit to them and see them through if we want to succeed with the parts we have. Thus the Panthers find themselves where we always seem to. We are a team that is as capable of going on a deep playoff run as we are forcing a total rebuild in the next two years. But for what it’s worth, I think it’s going to be a strong, “Eureka!” type season where everything finally comes together. For the sake of Rivera and company, I hope it does.
submitted by BlindWillieJohnson to nfl [link] [comments]

[OC] Building an NFL Draft Model using Machine Learning

Happy Sunday nfl. Like most of the users here, I get draft-obsessed every February when the Combine comes around. Well, this year I decided to do something about it by building a draft model. If you're not interested in the details, you can stop right here and click the links below.
 
Model outputs from validation can be viewed here: https://docs.google.com/spreadsheets/d/1-ooQ4UTafyFOTWDtbYGmPgdHfspY8bci45tUS6I5-LU/edit?usp=sharing
Album of select draft prospect profiles: https://imgur.com/a/SCdkLj1
I've created some simple player visual dashboards which present position-specific percentile rankings in performance and athleticism. Each of these refer to either neutralized statistics, or engineered features, so "Tackles" is more accurately "Neutralized Tackles per Game" and "Speed" is actually "Speed Score". If anyone has requests to see other players, let me know and I'll try to cover all of them.

Research Goal

To build an NFL draft model capable of producing meaningful player predictions. I had originally planned to do so using a fuzzy Random Forest trained on NFL Combine and Pro Day physical measurements, individual and team college statistics, and engineered features. The model produced superior results when treating physical measurements as crisp rather than fuzzy, which was surprising but nonetheless forced me to change my approach.
Random Forest model is appropriate for this dataset because of the relatively small number of observations (roughly 250-300 players per draft class) and the highly non-linear relationship between the input and output variables. Random Forests are fairly robust against overfitting, which is a concern when modelling noisy data.
Player performance is impacted by round and team selection in the draft - first-round selections receive more opportunities than seventh-round selections, different schemes fit some players better. Because of this the model performance can be greatly improved by including some regression to draft selection or, in the case of test data, public rankings.

Model Output

I've decided to take the novel approach of using player ratings from EA Sports' Madden video game franchise as a proxy for player production, skill, and value. This is beneficial for a number of reasons. The first is that these ratings provide continuous output on a consistent scale across both years and positions; a player rated 99 overall is considered to be elite at their position, regardless of the unique responsibilities or challenges in quantifying performance specific to that position. The second reason is that Madden ratings predate modern quantitative evaluative metrics like those provided by Football Outsiders or Pro Football Focus.
Madden ratings explained - https://fivethirtyeight.com/features/madden/#
Overall ratings are calculated using position-specific formulas that weight individual attributes like speed, strength, and tackling. Ratings are updated each year through a Bayesian-like process of weighing new information to update old. To aggregate ratings for each player, I use a 5-part mean which includes ratings in Years 1-4 and Peak rating.
NFL rookie contract length is 4 seasons (along with a fifth year club option for first-round picks), while the average career length in the NFL is less than 4 years. As such, when building a draft model is makes sense to only consider production accrued during the first 4 years of a player's career.
Year 1 represents the Madden rating given to each player following their rookie season. For this reason, the final year for which complete data is available is the 2014 draft class (with Madden 19 providing Year 4 ratings). This decision was made to better capture NFL success, as rookie player ratings are highly dependent on draft order. For example, in Madden 2008 rookie #1 overall pick JaMarcus Russell was awarded an overall rating of 82, just 1 point lower than #3 overall pick Joe Thomas. The next year, Russell's rating was 83, while Thomas was a 97 overall. By Madden 2010, Russell was given a rating of 72 overall, while Thomas maintained his 97 overall rating. Year 3 and Year 4 ratings have been given double weight for the same reason, with the added effect of lowering the ratings of players who were not able to stay in the league for at least 4 years.
While this metric on the whole does a good job of ranking player talent and production, it is blind to players who peaked later in their careers or those who had short careers. Notable examples of each include Eric Weddle (84.6 rating, eventual 2x All Pro, 6x Pro Bowl) and Jon Beason (95.4 rating, 1x All-Pro, 3x Pro Bowl). Weddle did not reach his peak until after re-signing with the Chargers as an unrestricted free agent prior to the 2011 season, and could have presumably reached his peak while playing for another team. Beason suffered an Achilles injury during the 2011 season and eventually lost his job with the Panthers, starting in only 26 games in the years following his rating window. Beason would have been eligible to sign as a free agent following the 2011 season had the Panthers not offered a contract extension.
In the NFL, the drafting team maintains the exclusive right to employ each player for 4 years following their selection, thus it is incumbent upon the team to select and develop players who provide the most value during that period. For that reason I stand by the decision to evaluate draft selections only on a player's first 4 years in the league.

Dataset

I wrote several web scraping programs to pull data from NFL Draft Scout (an excellent resource for Combine data, and the only source I'm aware of that includes Pro Day data), Pro-Football-Reference, and CFB Reference (both Sports-Reference-operated sites, easily the best sources for football statistics in the NFL or FBS).
The dataset covers the 2006-2014 draft classes and includes players who were ranked in NFL Draft Scout's top 300 in their draft year. I have removed all quarterbacks, kickers, punters, long snappers, and fullbacks due to the relatively small sample sizes or extreme specialization that each position requires. It might be valuable to evaluate these positions later – particularly quarterbacks – but for now the model focuses exclusively on 13 "skill" positions, bucketed into 7 position groups.
The dataset restrictions exclude some notable players ranked outside of the top 300, both drafted and undrafted, who went on to varying degrees of success in the NFL. At the top extreme are 4-time All Pro Antonio Brown and Super Bowl LIII MVP Julian Edelman. But while many players on this list never played a down in the NFL, it is important to be aware of which players are excluded and it may be worthwhile to expand the dataset in the future.
I have removed players from the dataset whose NFL careers were cut prematurely short either voluntarily or involuntarily (due to injury, not ability). These players' ratings (or lack thereof) are not representative of their production and thus only serve to complicate the dataset and confuse any modeling attempts. Examples include Aaron Hernandez, Gaines Adams, and Chris Borland. The list is as long as it is depressing.
There is also a subset of players who drastically changed position upon entering the league. This is contrary to less extreme position changes (tackle to guard, cornerback to safety), which occur frequently. These players have been removed because their college statistics create noisy data. Examples: Denard Robinson, Devin Hester, J.R. Sweezy.

College Statistics

College statistics have been collected and cleaned at the FBS level from Sports Reference. Using college statistics is important because they provide information on a player's in-game performance. However, college football styles vary greatly among teams and have changed over time. Therefore we must control for differences in pace and style of play when considering college numbers. Rather than attempt to fit a model on raw season total statistics, I've decided to use neutralized per game statistics under the following parameters:
 
 
To illustrate this point let's look at Calvin Johnson and Michael Crabtree, who were both highly productive college wide receivers selected early in the first round.
 
 
The two statlines appear very similar without context. It's easy to make this distinction empirically, but little effort has been made to translate college statistics into more informative data. Johnson and Crabtree put up similar overall numbers, but Crabtree did it in an air raid style offense that relied heavily on passing while Johnson played on a more balanced offense.
 
 
When we neutralize both players' statistics, we can better compare each player's level of production.
 
 
Compare those numbers to each player's NFL career statistics:
 
 
This is a cherry-picked example but it does well to show that while raw statistics are not to be trusted, college data when put into the proper context can be made more predictive. On a larger scale, we can compare RMSE of the model when including raw college statistics compared to pace- and schedule-neutralized statistics. Controlling for strength of schedule does not improve the predictiveness of the model, but controlling for pace and style of play does have a significant effect.
 
Neutralization RMSE
Raw per Game 8.065
Pace-Neutralized per Game 8.029
Pace- and Schedule-Neutralized per Game 8.058
 
Here's the full stat list, with a few notable performers:
 
Offensive Statistics
 
Defensive Statistics
 

NFL Combine and Pro Day Measurements

The final major inputs of the draft model are the physical measurements taken at the NFL Combine and university Pro Days. Pro Day measurements are harder to come by due to their decentralized and often scarcely reported nature. Fortunately, NFL Draft Scout has maintained a database of reported Pro Day measurements spanning the years in our dataset.
There is an enormous benefit in using Pro Day measurements in a model like this. It allows for a larger training set by including data on players who were not invited to the NFL Combine, but also provides much more complete data because not all players who attend the combine perform the full slate of workouts. This lessens the need for imputation and reduces uncertainty.
However, there is bias observed in Pro Day measurements. Pro Days are typically scheduled in the weeks following the NFL Combine, giving players more time to train for the specific physical events. Furthermore, they often take place at the players' home campuses in environments in which the players feel more comfortable. Lastly, many events (most notably the 40-yard dash) are hand-timed at Pro Days, leading to better reported times than the electronic times at the Combine. Each of these factors contributes to improvement in every event among the population of players who participated both at the NFL Combine and at their university Pro Day.
 
Players who participated in both NFL Combine and Pro Day
Measurement Combine Pro Day n Sigma Adjustment
40 Yard Dash 4.80 4.70 831 0.076 + 0.07
20 Yard Split 2.79 2.71 733 0.065 + 0.06
10 Yard Split 1.68 1.62 739 0.057 + 0.04
Bench Press 20.0 reps 21.7 reps 254 2.556 - 1.2
Vertical Jump 31.7" 33.6" 593 2.342 - 1.3"
Broad Jump 112.9" 115.2" 481 4.356 - 1.6"
20 Yard Shuttle 4.46 4.42 424 0.155 + 0.03
3 Cone Drill 7.34 7.22 342 0.223 + 0.08
 
In order to correct for this bias, I've (somewhat arbitrarily) chosen to shift recorded Pro Day measurements by 70% of the mean delta. Even when we correct for some of the systematic bias observed in Pro Day measurements, we must also recognize that most physical measurements aren't static. Some players aren't performing at maximum physical capacity on the day of the Combine, occasionally players injure themselves during their workout, and the measurements aren't always recorded with perfect accuracy or consistency.
A dataset with this much uncertainty lends itself well to fuzzy set theory. In simple terms, this will allow us to consider not only a player's recorded 40 yard time of 4.40, but will also consider some probability that their "true" speed is 4.39 or 4.43. So when the model attempts to predict NFL success given a player's 40 yard dash time, it's not based on a singular number but rather a distribution of times centered around that number.
Fuzzy Set Theory explanation - https://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/sbaa/report.fuzzysets.html
My approach is to generate a random forest model on the discrete data, then fit n iterations on randomly shuffled data to generate a distribution of outcomes for each player. This "shuffling" will occur randomly for each measurement using a normal distribution centered around the discrete number, with sigma equal to half of the standard deviations recorded above.
In a single random forest, data is crisply split by decision trees based on discrete information. But with enough randomly shuffled iterations, the trees are no longer binary decisions but rather probabilistic ones centered on each measurement's distribution. This is particularly relevant for players who may have measurements near decision tree boundaries. Two players with sprint times separated by mere hundredths of a second are not appreciably different in speed, but a random forest might classify them as such. The purpose of shuffling is not to fundamentally change each player's physical characteristics, rather to acknowledge measurement uncertainty. My belief is that this will improve the model outputs over a large enough number of trials.
We have a wealth of NFL Combine and Pro Day data but not every player has participated in every drill, so we'll need to fill in missing values. Because many of these physical measurements are correlated and most football positions require some degree physical specialization (size, speed, etc.), I've chosen a k nearest neighbor imputation method. The belief is that if Players A and B are similar in terms of position, size, speed, and quickness, then the two players will also have similar strength or jumping ability. The exceptions are draft age and wingspan, which can be reasonably predicted using population means.

Engineered Features

Perhaps the most essential component of a machine learning model is feature engineering.
Modern feeling toward physical measurements taken at the Combine is highly dubious, and I agree that each measurement taken in isolation cannot alone adequately define athleticism, much less predict success. However, there exist more complex metrics which can better perform both tasks across a large enough sample.
 
Body Mass Index (BMI)
 
Speed Score
 
Height-Adjusted Speed Score
 
Vertical Jump Power
 
Broad Jump Power
 
Quickness Score
 
Weight-Adjusted Bench
 
Catch Radius
 
The models also include several features designed to summarize the collection of college statistics being used.
 
Offensive Usage
 
Defensive Disruption
 
S&P Market Share

Cross-Validation and Tuning

I've tuned the model using stratified k-fold cross validation, leaving out each draft class as OOB observations. As a result, every player has been included in both the training and validation sets. Each position group has been fit with its own unique hyperparameters to optimize predictions.
 
Hyperparameters by Position Group
Position Number of Trees Max Depth Max Features Min Leaf Samples
WR 100 5 10 3
FS 250 5 10 1
CB 50 10 20 2
SS 40 10 10 2
ILB 30 15 5 2
RB 40 10 10 1
TE 20 10 3 2
EDGE LB 50 5 10 2
EDGE DL 250 15 10 2
C 50 5 5 3
DT 100 3 10 1
OT 20 5 3 2
OG 20 10 5 2
 
Additionally, the model performed best when aggregating predictions from 3 randomized sets, as shown in the plot below. However, this fuzzy approach failed to outperform discrete features during cross-validation. I expected the opposite, but it seems treating each measurement as precise leads to the best fit.
 
RMSE Using Various Methods
Method RMSE
Discrete 8.027
1 Random set 8.122
2 Random sets 8.069
3 Random sets 8.063
5 Random sets 8.098
10 Random sets 8.115

Results and Further Research

By and large the model does surprisingly well considering the lack of more traditional evaluative inputs. NFL teams have the resources of scouting departments providing more detailed player evaluation, experienced coaching staffs evaluating personnel fits, and front offices to balance financial considerations and positional value. Each of these factor into draft decisions and improve ranking methods beyond the scope of this model.
 
Model results by position
Position RMSE n Most Important Features
WR 7.523 314 Underclassman, Usage, Age, Srimmage Yards, Total TD, Receiving Yards
FS 7.621 128 S&P Share, Age, SOS, 20-Yard Shuttle, Height, 40-Yard Dash
CB 7.604 292 Age, Quickness Score, Height-Adjusted Speed Score, S&P Share, Height, 20-Yard Dash
SS 7.437 107 Run Stuffs, BMI, Defensive Disruption, Quickness Score, Tackles, TFL
ILB 7.649 291 S&P Share, Age, Tackles, Height-Adjusted Speed Score, Vert Power
RB 8.121 194 Rush TD, Rush Yards, Age, Total TD, Scrimmage Yards, Height-Adjusted Speed Score
TE 8.097 139 Scrimmage Yards, Receiving TD, Receiving Yards, Offensive Usage, Hand Size
EDGE LB 8.846 90 S&P Share, Disruption, Catch Radius, Tackles, Weight, Age
EDGE DL 7.445 190 Age, TFL, Weight, Height-Adjusted Speed Score, Quickness Score, Underclassman
C 8.948 82 3-Cone, Weight, Broad Jump, Quickness Score, Hand Size, Age
DT 7.823 211 S&P Share, TFL, Tackles, Disruption, Run Stuffs, 3 Cone
OT 8.882 222 Age, Vert Power, Arm Length, Speed Score, Weight
OG 8.819 140 Adjusted Bench, 20-Yard Dash, Catch Radius, Quickness Score, Age, Weight
 
When properly optimized, the model can achieve RMSE below 8 during cross-validation. Unsurprisingly, it struggles most with offensive linemen, who lack individual statistics. In particular it struggles with centers, whose responsibilities in the NFL are as much mental as physical. Interestingly, NFL teams have had great success evaluating centers, as 4 of the 5 first rounders were named to All-Pro teams in their careers, and all made the Pro Bowl at some point.
As mentioned in the introduction, the model could be improved substantially by including draft selection or consensus rankings. Furthermore, team-specific random effects could likely explain some of the residuals. I may eventually explore these research questions, but my short-term priorities are on visualization and presentation of data.
 
If you've made it this far, check out my github for the source code: https://github.com/walt-king/NFL-draft-research
This was created using Python for web scraping, data collection, modelling, and visuals. I used R to create the player dashboards. Comments, thoughts, and feedback all greatly appreciated.
submitted by dataScienceThrow1 to nfl [link] [comments]

MAME 0.203

MAME 0.203

With Hallowe’en basically over, the only thing you need to make October complete is MAME 0.203. Newly supported titles include not just one, but two Nintendo Game & Watch classics: Donkey Kong and Green House, and the HP 9825B desktop computer. We’ve added dozens of new versions of supported systems, including European bootlegs of Puck Man, Ms. Pac-Man, Phoenix, Pengo and Zero Time, more revisions of Street Fighter II and Super Street Fighter II, and a version of Soldier Girl Amazon made under license by Tecfri.
There are major improvements to plug-in TV games in this release, specifically systems based on the XaviX and SunPlus µ'nSP processors. The Vii is now playable with sound, and the V.Smile can boot games. Tiger Game.com emulation has come to the point where all but one of the games are playable. Some long-standing issues with Tandy CoCo cartridges have been fixed.
It isn’t just home systems that have received attention this month: Namco System 22 emulation has leapt forward. Yes, the hit box errors making it impossible to pass the helicopter (Time Crisis) and the tanks (Tokyo Wars) have finally been fixed. On top of that, video emulation improvements make just about everything on the system look better. In particular, rear view mirrors in the driving games now work properly. If that isn’t enough for you, the code has been optimised, so there’s a good chance you’ll get full speed emulation on a modern PC. There have been less dramatic improvements to video emulation in other Namco and Tecmo systems, and CPS-3 row scroll effects have been implemented.
MAME 0.203 should build out-of-the-box on macOS “Mojave” with the latest Xcode tools (provided your SDL2 framework is up-to-date), a number of lingering debugger issues have been fixed, and it’s now possible to run SDL MAME on a system with no display. MAME’s internal file selection menus should behave better when you type the name of a file to select it.
MAME 0.203 is a huge update, touching all kinds of areas. You can get the source and Windows binary packages from the download page.

MAMETesters Bugs Fixed

New working machines

New working clones

Machines promoted to working

New machines marked as NOT_WORKING

New clones marked as NOT_WORKING

New working software list additions

Software list items promoted to working

New NOT_WORKING software list additions

Source Changes

submitted by cuavas to emulation [link] [comments]

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