If you’re reading this, thanks for watching!! Just wanted to emphasize that I am NOT AN EXPERT at this stuff. I purely made this video because I spent way too long on this project to not try to share the story with others! If you find videos like these interesting, let me know! My top priority is still CFB 25 but maybe someday I’ll start a second channel for more general sports analytics topics
I feel like for a video like this it would be helpful to be showing more visuals to back up what you’re trying to convey. You said that your model was more accurate than the ESPN predictions, it’d be dope to see numbers on that just to see the magnitude cause that’s really cool
@@natemenzel6026 yeah I agree. I could have taken the time to show snippets of actual code or other stuff like that. Unfortunately this video has been sitting on my computer 80% completed for like 3 weeks now, so I willingly put out a B- video bc I was scared that if I took the time to get all the visuals, I’d never release it But if this goes well and ppl like it, I DEF have gotta do a better job of making this less of a talking head video
Really fascinating video. You're right it's definitely hard to measure success with the absolute chaos and randomness of fantasy football. Maybe a thing worth considering is finding out which players the model loved like you mentioned top QB's and veteran WR's, then compare them to other players in that draft range. Or see if it predicted any sleeper late round hits. Probably easier to look at specifics then in totality. Keep up the great work, I love your analytics and data stuff. You obviously know your stuff.
Thank you!! And yeah, i think now that i have all this awesome data, i need to try to find out if QBs really do matter I’ll have to try to think a bit about how to measure that
@@maxplayscfb One reason the model might overvalue top qbs and veterans is because it’s predicting/maximizing median performance when in reality most of us care about getting first in the league, which almost never comes from a median performance if that makes sense. You could have it predict probability of winning the league or something like that. Then you could stratify the data by year, leaving the last year out of the model training. Then test how accurate its predictions are on team winning probabilities for each pick and each team for each league. It’d be interesting to see how the league size affects the value of variance, and whether or not it still values those top qbs and veterans.
I feel so lucky I found one of your videos last week and I have thoroughly enjoyed your work, perspective and analytical thinking towards CFB and now this video just sent me over the moon with excitement. 😅 I love this style of video and will be here for every single second of any further videos like this one. The idea I read in another comment about applying additional chart and graphics of analytical data would be fantastic. Thank you for sharing your wonderful mind with us here! I really appreciate you.
Great video! I’ve thought of doing something similar given my 25+ years playing FFL. I’m excited to see how AI will change things. Especially throughout the season with waivers, injuries, trades, etc…
Hey Max, I’m a big fan and I love this video. I’m also a Data Scientist and have spent my fair share of time trying to solve this problem in a similar way. Here are a few of my thoughts on what else you may want to consider and what I have found is helpful: 1. When evaluating projected player performance, I’ve always liked including projection ceilings and floors (available on FantasyPros) as a part of the calculation. This gives me an idea of the risk a certain player has or how high a player could go in a season. You’d have to set the model to be aggressive or conservative, might help you get a breakout rookie WR. 2. I wonder if your QB preference is skewed at all by points scored. You are building teams to score the most points, QBs score the most points. What you really need to solve is: 3. Drop off based on remaining players. Sometimes there is a huge difference between WR 5 and WR 8. Sometimes there’s almost no drop off between RB 15 and RB 28. This is why top QBs are rarely necessary for a winning team. QB 3 may average just a few points more than QB 8, but that is a much larger cavern for WR. Knowing the position you can wait on (like your TE example) is the key. But, that may have less to do with what your league mates are drafting and more to do with who are the remaining players. 4. In my opinion, this is less of an application for some type of predictive model and more an application for an optimization engine. I’m curious what sort of optimization simulations you could run on this topic. Finally, here is a link to a paper that one of my coworkers was a co-author of on the draft optimization topic. Definitely recommend it if you want to research more: www.sciencedaily.com/releases/2007/08/070823170012.htm I’d love to connect further to discuss if you’d like! Keep up the great work!
thank you for reaching out and for such a detailed viewpoint on this project! Will definitely look into that paper. Would also love to talk more about this specific application and your background on it! My email for this profile is maxplayscfb@gmail.com, id love to connect!
This is awesome man. Based on the plots you’re showing, it looks like you’re using R to do your analysis. If you know python, I am building a strongly typed ESPN API wrapper package that gives the user access to live and historical data. I plan to build some machine learning models and possibly stochastic simulation models to predict player stats, team stats, etc.. I know R (possibly more than python) so if you’re looking to expand your model or looking to team up let me know. The benefit of using the ESPN API is you get access to specific venues, coaching, referees, lines, injuries, and A LOT more.
This is an awesome project! It's very interesting to see the model prioritize early QB like many first time drafters would. As far as inputs to improve the model I have a couple thoughts/suggestions: -In addition to player ADP it would be worth considering sportsbooks' projections of players' seasonal performance. I would guess that sportsbooks projections would actually beat fantasy positional ADP alone. -Inclusion of WAR in some capacity would be interesting as it takes into account positional importance and player performance. WAR makes a distinction between the relative value difference between adjacent players at the position in the draft, whereas your choice of input for positional importance may not allow the model to recognize similarly valued players and tier gaps. (Josh Allen may have been in a tier of his own this year, but your model may not have been able to recognize if there were 5 similar QBs to target in the early rounds).
Also although I understand the tradeoff of trying to predict more than 12 weeks, there is a considerable loss in limiting it to 12 weeks. For example, in his rookie season ARSB averaged 7 ppg weeks 1-12 and 25 ppg weeks 13-17. This late surge during the fantasy playoffs is not atypical for rookie wide receivers and I imagine that finding these high upside young receivers is a big piece of winning fantasy championships.
If you want a general idea of how much better it is to use the model, you’d see how many of your 5 teams make the playoffs when you do vs when you don’t use it. If last year you didn’t use it and 3/5 teams missed the playoffs but you use it this year and 5/5 teams make the playoffs, you know it put you in an advantageous position. (Trades and waiver wire aside).
good note -- the github repo for the WR project is github.com/iam-max-thompson/predicting_fantasy_points fair warning, it was for a school project so the repo is UGLY but there's a pretty comprehensive write up in there I have not added anything to github for this current iteration because it is a COMPLETE MESS. my goal over the next offsason is to clean it up, make it repeatable, and then make it public
This doesn’t seem terribly far off of my basic draft strategy as a human player. In those early rounds, its absolutely better to go with a player that you know is going to perform barring injury. And outside of Breece Hall just simply not being used by a team with a QB with currently below league average play, Ive been happy with all of my picks in the first 7 rounds in all 3 of my leagues. Though instead of rushing to get a QB early, I try to get a top tier TE, or maybe even 2. As with the TE position, there’s typically only about 4 consistent point scoring TEs in the league, and getting 1 or even 2 both ensures that you have an advantage at the position, and limits what other teams can do at that position. Plus in all of my drafts Captain Kirk and Baker where available in the 12th round and later. Who are two QBs that you can depend on to get a decent points haul every week, yet somehow seem to start the wrong one of the two every bloody week Then in the later rounds, I try to pick up rookies and backup running backs. As they are typically good bank options with a massive upside, usually in the event of injury for when lets say the consensus number 1 pick gets put on LTIR Ive also completely ignored picking up defenses until the last two rounds, and pick up the surprise defense (or 2) of the season on the waiver wire to get me through the season, or just pick the team that’s playing against the Panthers
im really hoping there are people that sit at the intersection of sports + analytics + youtube, and good to know i found someone!! ill keep trying to make more vids like this when i find time
Honestly I'd be curious how much a good draft affects final league ranking. Obviously every win is good, but maybe as a whole a good draft is overrated to some extent
yeah thats a really good point -- itd be interesting to be like: Your draft grade is an A+ Mine is a C- Are you 10% more likely to make the playoffs? 50% more likely? etc
So was this trained on just last year? Because all the early RBs busted and the old wr’s killed it (Keenan and Mike Evans were like top 3 first 12 games last year I think). Also hurts and Allen were smash early picks last year so that makes sense. Feels like it just made its strat from what worked in drafts last year
Trained on drafts from 2018-2023 (however to me you’re right - that’s still not enough. If you look at the past half decade of fantasy data only, you’d prob come to the conclusion that the top ranked TE is worth it. In reality, is that true, or is that just talking about Travis kelce for 5 years???🤣🤣 except this year)
@@maxplayscfb yeah were years evenly sampled? Because I’d guess there were a lot more sleeper leagues last year than prior years. But idk I might just think that because I didn’t learn about sleeper until last year
@@maxplayscfb and I think it’s really cool it waited on te when everyone else had one. Maybe it also notices that there’s not much of a difference between a lower te and last round level te. Njoku laporta McBride were like last round picks last year LOL. Kinda repeating this year with Kraft ertz maybe
If you’re reading this, thanks for watching!! Just wanted to emphasize that I am NOT AN EXPERT at this stuff. I purely made this video because I spent way too long on this project to not try to share the story with others!
If you find videos like these interesting, let me know! My top priority is still CFB 25 but maybe someday I’ll start a second channel for more general sports analytics topics
You’re welcome
Please do! I would greatly appreciate a channel like that. I love numbers. 🤓😅🙂↕️
I feel like for a video like this it would be helpful to be showing more visuals to back up what you’re trying to convey. You said that your model was more accurate than the ESPN predictions, it’d be dope to see numbers on that just to see the magnitude cause that’s really cool
@@natemenzel6026 yeah I agree. I could have taken the time to show snippets of actual code or other stuff like that. Unfortunately this video has been sitting on my computer 80% completed for like 3 weeks now, so I willingly put out a B- video bc I was scared that if I took the time to get all the visuals, I’d never release it
But if this goes well and ppl like it, I DEF have gotta do a better job of making this less of a talking head video
It’s still fire nonetheless, but seeing the data would be a sick toucg
@@natemenzel6026I agree 100%
Really fascinating video. You're right it's definitely hard to measure success with the absolute chaos and randomness of fantasy football. Maybe a thing worth considering is finding out which players the model loved like you mentioned top QB's and veteran WR's, then compare them to other players in that draft range. Or see if it predicted any sleeper late round hits. Probably easier to look at specifics then in totality. Keep up the great work, I love your analytics and data stuff. You obviously know your stuff.
Thank you!! And yeah, i think now that i have all this awesome data, i need to try to find out if QBs really do matter
I’ll have to try to think a bit about how to measure that
@@maxplayscfb One reason the model might overvalue top qbs and veterans is because it’s predicting/maximizing median performance when in reality most of us care about getting first in the league, which almost never comes from a median performance if that makes sense. You could have it predict probability of winning the league or something like that. Then you could stratify the data by year, leaving the last year out of the model training. Then test how accurate its predictions are on team winning probabilities for each pick and each team for each league. It’d be interesting to see how the league size affects the value of variance, and whether or not it still values those top qbs and veterans.
Amazing video! Hope you continue making these types of videos, trying (and failing) to reduce sports to numbers is honestly a very fun passtime
thank you!! at some point ill talk sports gambling, had a fun failure of a project once there too
I feel so lucky I found one of your videos last week and I have thoroughly enjoyed your work, perspective and analytical thinking towards CFB and now this video just sent me over the moon with excitement. 😅 I love this style of video and will be here for every single second of any further videos like this one. The idea I read in another comment about applying additional chart and graphics of analytical data would be fantastic. Thank you for sharing your wonderful mind with us here! I really appreciate you.
That’s awesome, thank you for the kind words!! And yeah, I’ll step up my chart game next time for sure
Super interesting but I don’t know how effective this type of thing will ever work. See CMC, Mahomes, Brian Thomas Jr this year.
My favorite TH-camr by far
yo thank you!!!! i appreciate anyone that watches both the CFB25 stuff but also the random stuff like this LOL
Legit for not rushing the TE pick . Great video
Great video! I’ve thought of doing something similar given my 25+ years playing FFL. I’m excited to see how AI will change things. Especially throughout the season with waivers, injuries, trades, etc…
Thank you!! And yeah, week to week roster management sounds like a very interesting area to use analytics - but also a very hard area
Hey Max, I’m a big fan and I love this video. I’m also a Data Scientist and have spent my fair share of time trying to solve this problem in a similar way.
Here are a few of my thoughts on what else you may want to consider and what I have found is helpful:
1. When evaluating projected player performance, I’ve always liked including projection ceilings and floors (available on FantasyPros) as a part of the calculation. This gives me an idea of the risk a certain player has or how high a player could go in a season. You’d have to set the model to be aggressive or conservative, might help you get a breakout rookie WR.
2. I wonder if your QB preference is skewed at all by points scored. You are building teams to score the most points, QBs score the most points. What you really need to solve is:
3. Drop off based on remaining players. Sometimes there is a huge difference between WR 5 and WR 8. Sometimes there’s almost no drop off between RB 15 and RB 28. This is why top QBs are rarely necessary for a winning team. QB 3 may average just a few points more than QB 8, but that is a much larger cavern for WR. Knowing the position you can wait on (like your TE example) is the key. But, that may have less to do with what your league mates are drafting and more to do with who are the remaining players.
4. In my opinion, this is less of an application for some type of predictive model and more an application for an optimization engine. I’m curious what sort of optimization simulations you could run on this topic.
Finally, here is a link to a paper that one of my coworkers was a co-author of on the draft optimization topic. Definitely recommend it if you want to research more: www.sciencedaily.com/releases/2007/08/070823170012.htm
I’d love to connect further to discuss if you’d like! Keep up the great work!
thank you for reaching out and for such a detailed viewpoint on this project! Will definitely look into that paper. Would also love to talk more about this specific application and your background on it! My email for this profile is maxplayscfb@gmail.com, id love to connect!
@@maxplayscfb Sent you an email 10/9
This is awesome man. Based on the plots you’re showing, it looks like you’re using R to do your analysis. If you know python, I am building a strongly typed ESPN API wrapper package that gives the user access to live and historical data. I plan to build some machine learning models and possibly stochastic simulation models to predict player stats, team stats, etc.. I know R (possibly more than python) so if you’re looking to expand your model or looking to team up let me know.
The benefit of using the ESPN API is you get access to specific venues, coaching, referees, lines, injuries, and A LOT more.
Forgot to mention - play-by-play data
this vid is super cool concept wise but would really benefit from some more visuals boss. great stuff!
This is an awesome project! It's very interesting to see the model prioritize early QB like many first time drafters would. As far as inputs to improve the model I have a couple thoughts/suggestions:
-In addition to player ADP it would be worth considering sportsbooks' projections of players' seasonal performance. I would guess that sportsbooks projections would actually beat fantasy positional ADP alone.
-Inclusion of WAR in some capacity would be interesting as it takes into account positional importance and player performance. WAR makes a distinction between the relative value difference between adjacent players at the position in the draft, whereas your choice of input for positional importance may not allow the model to recognize similarly valued players and tier gaps. (Josh Allen may have been in a tier of his own this year, but your model may not have been able to recognize if there were 5 similar QBs to target in the early rounds).
Also although I understand the tradeoff of trying to predict more than 12 weeks, there is a considerable loss in limiting it to 12 weeks. For example, in his rookie season ARSB averaged 7 ppg weeks 1-12 and 25 ppg weeks 13-17. This late surge during the fantasy playoffs is not atypical for rookie wide receivers and I imagine that finding these high upside young receivers is a big piece of winning fantasy championships.
Bro drop this every year, lets see how you do in leagues every year just based on the ai’s drafting
heck yeah, i will also make a follow up vid with how things went this season at the end of it all!
@@maxplayscfb should be interesting bro
Justin tucker is easily a 1st rounder.. and I LOVE this sort of stuff
Thanks for watching!
This made me chuckle 🤣 been telling my cowboy fan buddy Dallas Kicker has been their most consistent fantasy player all year.
If you want a general idea of how much better it is to use the model, you’d see how many of your 5 teams make the playoffs when you do vs when you don’t use it. If last year you didn’t use it and 3/5 teams missed the playoffs but you use it this year and 5/5 teams make the playoffs, you know it put you in an advantageous position. (Trades and waiver wire aside).
this is really cool! do you also have a video or github on your wide receiver model? i’m really intrigued by it too
good note -- the github repo for the WR project is github.com/iam-max-thompson/predicting_fantasy_points
fair warning, it was for a school project so the repo is UGLY but there's a pretty comprehensive write up in there
I have not added anything to github for this current iteration because it is a COMPLETE MESS. my goal over the next offsason is to clean it up, make it repeatable, and then make it public
This doesn’t seem terribly far off of my basic draft strategy as a human player. In those early rounds, its absolutely better to go with a player that you know is going to perform barring injury. And outside of Breece Hall just simply not being used by a team with a QB with currently below league average play, Ive been happy with all of my picks in the first 7 rounds in all 3 of my leagues.
Though instead of rushing to get a QB early, I try to get a top tier TE, or maybe even 2. As with the TE position, there’s typically only about 4 consistent point scoring TEs in the league, and getting 1 or even 2 both ensures that you have an advantage at the position, and limits what other teams can do at that position. Plus in all of my drafts Captain Kirk and Baker where available in the 12th round and later. Who are two QBs that you can depend on to get a decent points haul every week, yet somehow seem to start the wrong one of the two every bloody week
Then in the later rounds, I try to pick up rookies and backup running backs. As they are typically good bank options with a massive upside, usually in the event of injury for when lets say the consensus number 1 pick gets put on LTIR
Ive also completely ignored picking up defenses until the last two rounds, and pick up the surprise defense (or 2) of the season on the waiver wire to get me through the season, or just pick the team that’s playing against the Panthers
ooooo thank you algorithm, this is exactly my kinda content
im really hoping there are people that sit at the intersection of sports + analytics + youtube, and good to know i found someone!! ill keep trying to make more vids like this when i find time
I have Jalen Hurts and a ton of WR’s. Let’s see how I do, this year!! So far I’m 3-2.
hopefully the WRs pan out!!
Honestly I'd be curious how much a good draft affects final league ranking. Obviously every win is good, but maybe as a whole a good draft is overrated to some extent
yeah thats a really good point -- itd be interesting to be like:
Your draft grade is an A+
Mine is a C-
Are you 10% more likely to make the playoffs? 50% more likely? etc
This shit itches my brain
Heck yeah glad to scratch that itch
@@maxplayscfb I’m starting community college soon, what associates program would you recommend to transfer into an undergrad data science program
So was this trained on just last year? Because all the early RBs busted and the old wr’s killed it (Keenan and Mike Evans were like top 3 first 12 games last year I think). Also hurts and Allen were smash early picks last year so that makes sense. Feels like it just made its strat from what worked in drafts last year
Trained on drafts from 2018-2023 (however to me you’re right - that’s still not enough. If you look at the past half decade of fantasy data only, you’d prob come to the conclusion that the top ranked TE is worth it. In reality, is that true, or is that just talking about Travis kelce for 5 years???🤣🤣 except this year)
@@maxplayscfb yeah were years evenly sampled? Because I’d guess there were a lot more sleeper leagues last year than prior years. But idk I might just think that because I didn’t learn about sleeper until last year
@@maxplayscfb and I think it’s really cool it waited on te when everyone else had one. Maybe it also notices that there’s not much of a difference between a lower te and last round level te. Njoku laporta McBride were like last round picks last year LOL. Kinda repeating this year with Kraft ertz maybe
hi'
Dang quick response