Re:Show HN: Applying Machine Learning to March Madness
by solidasparagus on Monday March 18, 2019 @06:16AM
(#19419897)
This is a fun project and there's plenty to learn even if you don't end up with a great model.
But if you really want to try to solve this problem, you're going to need more granular data - play-by-play, per-play lineup, injuries, player tracking if you can get your hands on it. The stats you are using are very lossy summaries of the season, so they aren't very strong predictive features.
From a technical POV, consider bucketing some of those per-game stats (e.g. 4 binary features representing which quartile the team's stat falls in compared to every other team that season). This can help to adjust for year-to-year differences. Work with pace-adjusted stats if you have access to them. Find a baseline accuracy by picking the simplest possible non-ML strategy and measuring how accurate that is (e.g. what is the accuracy of a model that always picks the better seed or W/L record?).
You need to adjust your training data to only use data that would have been available at the time of the game - if W/L or PPG includes data from future games, this is a form of data snooping and will probably give you results on your test set that won't generalize to the real world. Time-series snooping is a very easy mistake to make, but it's crucial to avoid it in order to build a good model.
Re:Show HN: Applying Machine Learning to March Madness
by ham_sandwich on Monday March 18, 2019 @10:41AM
(#19421413)
You’re right. Like a lot of other engineers, I once thought “ML+High level team info=$$$$” but quickly learned that you really can’t get an edge unless you’re digging into that more granular data and even then it’s really tough. It can be very hard to improve on simple linear models.
The odds coming out of Vegas are usually priced correctly. Sports markets are very efficient—although perhaps not as ruthlessy efficient as public equity markets. I would imagine there are still syndicates out there that are the “RenTec of sports betting” and just printing alpha.
Re:Show HN: Applying Machine Learning to March Madness
by QuackingJimbo on Monday March 18, 2019 @04:33AM
(#19419472)
> One in 9.2 quintillion. Those are the odds that you will correctly pick the winners of all 63 games played over the course of the tournament. Mathematically speaking, there are 2^63 (~9.2 quintillion) number of ways that you can fill the bracket
This is wrong. Many of the games are not even close to 50-50.
Re:Show HN: Applying Machine Learning to March Madness
by jsjohnst on Monday March 18, 2019 @08:08AM
(#19420399)
So even in the best case you list (which I share your skepticism of), you are 1,000x more likely to win the MegaMillions or Powerball jackpot than pick the perfect winning grid.
To put the odds of picking a perfect grid by hand in rough perspective, it would be like winning the lottery, boarding a plane that crashes into the ocean, being the only survivor floating in the ocean, but then are struck by lightning 3 times and yet you live, only to then be eaten by a shark, all in a single day.
Re:Show HN: Applying Machine Learning to March Madness
by dagw on Monday March 18, 2019 @08:41AM
(#19420547)
So even in the best case you list (which I share your skepticism of), you are 1,000x more likely to win the MegaMillions or Powerball jackpot than pick the perfect winning grid.
That makes me wonder what odds you'd get at the bookmakers for a perfect winning grid.
Re:Show HN: Applying Machine Learning to March Madness
by jsjohnst on Monday March 18, 2019 @07:55PM
(#19426592)
I think I remember seeing a prize somewhere of an obscene amount of money (memory is hazy, but was something like over $100M, maybe even a billion) for a perfect winning grid.
Re:Show HN: Applying Machine Learning to March Madness
by jjuel on Monday March 18, 2019 @10:18AM
(#19421174)
I mean do you really think NCAA basketball games are just a random coin toss? If that were the case wouldn't more than 1 16 seed have won a game? Wouldn't more lower seeds have won the whole tournament? In that case theoretically every team has the same chance to win it all yet the lowest seed ever to win was an 8 seed.
Re:Show HN: Applying Machine Learning to March Madness
by adeshpande3 on Monday March 18, 2019 @10:54AM
(#19421538)
Yeah tonmoy linked to the blog post that had the predictions. Unfortunately, the model pretty much predicted a higher probability for the higher seeded team in the matchup which lead to some relatively expected predictions. If I remember correctly, it led to a pretty average percentile (50-60) when I submitted it to the ESPN leaderboards.
There was a mainstream article written about the tech a while back, which had GIFs demonstrating simulations, but I failed to find it. Essentially, it would model what the players did versus what they should have done, optimally.
Re:Show HN: Applying Machine Learning to March Madness
by bitxbit on Monday March 18, 2019 @08:19AM
(#19420448)
That is very cool. I was thinking something more macro where you can see the expected outcome based on different aspects of game strategies such as pace, mix of plays, etc.
Re:Show HN: Applying Machine Learning to March Madness
by shehryarrr on Monday March 18, 2019 @09:56AM
(#19420999)
I actually used to work for a company where we did exactly that. We did it based off of in game footage that we would stat and annotate it was a really interesting product. I think Second Spectrum does something similar now for pro basketball teams.
But if you really want to try to solve this problem, you're going to need more granular data - play-by-play, per-play lineup, injuries, player tracking if you can get your hands on it. The stats you are using are very lossy summaries of the season, so they aren't very strong predictive features.
From a technical POV, consider bucketing some of those per-game stats (e.g. 4 binary features representing which quartile the team's stat falls in compared to every other team that season). This can help to adjust for year-to-year differences. Work with pace-adjusted stats if you have access to them. Find a baseline accuracy by picking the simplest possible non-ML strategy and measuring how accurate that is (e.g. what is the accuracy of a model that always picks the better seed or W/L record?).
You need to adjust your training data to only use data that would have been available at the time of the game - if W/L or PPG includes data from future games, this is a form of data snooping and will probably give you results on your test set that won't generalize to the real world. Time-series snooping is a very easy mistake to make, but it's crucial to avoid it in order to build a good model.
Interesting work, thanks for sharing!
The odds coming out of Vegas are usually priced correctly. Sports markets are very efficient—although perhaps not as ruthlessy efficient as public equity markets. I would imagine there are still syndicates out there that are the “RenTec of sports betting” and just printing alpha.
This is wrong. Many of the games are not even close to 50-50.
To put the odds of picking a perfect grid by hand in rough perspective, it would be like winning the lottery, boarding a plane that crashes into the ocean, being the only survivor floating in the ocean, but then are struck by lightning 3 times and yet you live, only to then be eaten by a shark, all in a single day.
That makes me wonder what odds you'd get at the bookmakers for a perfect winning grid.
Edit: it was a billion.
https://genius.com/Warren-buffett-billion-dollar-march-madne...
XD
https://adeshpande3.github.io/adeshpande3.github.io/Applying...
http://www.sloansportsconference.com/wp-content/uploads/2018...
There was a mainstream article written about the tech a while back, which had GIFs demonstrating simulations, but I failed to find it. Essentially, it would model what the players did versus what they should have done, optimally.