Can an artificial neural network predict the outcome of sports games? [closed]

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I was trying to find something original and fun to do with artificial neural networ开发者_JS百科ks (ANNs) as a personal/learning project and I though it would be cool if I could predict the results of sports games (especially NHL games).

I'm pretty sure it would be easy to evolve an ANN that can predict which team is most likely to win (usually the team with the better record). However, what I would like to do is create an ANN that would tell how likely the outcome is, similar to bookmaker odds.

Is this something an ANN can do? In the affirmative, what kind of success can I expect? I know I can't beat the bookmaker (at least not with a software solution). I want do this as a recreational project/challenge to myself. I don't expect to bet money on sports games with this project.

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Way back in the days of the IBM XT I played with a shareware ANN program to try and improve my chances on the British football (soccer) pools. This is a form of betting where you try and predict which football matches will result in draws. I assigned each team a number then looked back thorough past results and from them generated a single digit for the result. From memory it was 0 from a home win , 1 for an away win and 2 for a draw. Each result went on a single line in a training file. I would then run the training file through the program and generate the ANN settings. I would then look up the following Saturdays matches and feed them into the ANN then look for matches predicted as draws.

As the weeks went on my predictions of draws did definetly become more and more accurate. However ...

1) The XT was so slow that by Christmas it was taking 24 hours to generate the ANN settings from the training data. I really had better things to do with my precious (and expensive) PC.

2) Although it was better at predicting draws it wasn't predicting enough to actually win any money. Looking back I suppose the program had just worked out that Manchester United would always beat Sheffield United. This was more football knowledge than I had but not enough to win any money.

3) Entering the results into the training data and then generating the forthcoming matches data was taking me ages and to be honest sport bores me rigid.

So I gave up and didn't become a millionaire.

These days however PC's are much faster and much of the training data could be scraped from the web. But I still doubt it is a route to a fortune but its certainly an interesting project.

Ian

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A reply above stated:

I know that if the bookmakers odds could be beaten by an ANN, bookmakers would already be using one to fix their odds.

Bookmakers don't set the line based on their analysis of the teams - they set it based on their analysis of the betting public's opinion of the teams. An ideal line for the bookie is where he has exactly the same amount bet on each side of the line - then he is guaranteed a profit = the 'juice' on the losers' bets. They move the line as game approaches to try to keep that 50/50 split. Bookie may think Home team -5 is accurate line based on game analysis, but if he expects that will draw 2x $$ on the Home team he will not set the line at -5 - he will set at -7 or -8 - to where he expects to draw equal $$ for both -5 and +5 bets.

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ANNs are really good at pattern matching and prediction, so yes, odds are you could build an ANN that does what you want.

You'll need more than just team win/loss ratio to make it really effective however. Feed it stats for the players, too. For real effectiveness, try to include game-flow information... like which players are on the line for each play (for football, for example).

Ultimately, the biggest problem you'll run into (aside from the whole "writing the ANN" issue) is getting the data you need to feed it.

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I've done some stock market predictions with an AI and my conclusion is that it is not very hard to make an AI that gets good results with the historical data. Making winning transactions in the future is a different ballgame.

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I have just worked on this very problem (predicting English Premier League games) for the past 10 days, and ended up with very similar results using 3 different methods: SVM, Logistic Regression, and NN.

LR and NN will give probabilities. SVM outputs 0/1 (but it can be tweaked for probas too (I haven't tried yet).

I needed a "massive" (by my standards at least) feature set though (almost 300) and a good chunk of data (13 years worth).

Re. data, I got it from the web, simply.

Conclusion: I can just about match the bookies in terms of accuracy (predicting victories in my case). If I add the pre-match odds to the feature set, I get the exact same accuracy as the bookies (as expected), but no better (surely meaning my feature set is summarized in the bookies odds, and they have a little extra knowledge on top).

I'm sure there is a way to get better accuracy, either by improving the algos, or more likely by having extremely granular data (as in which players play which games, for how many minutes, and a lot of player-level historical stats, so as to build bottom-up models of team performance).

But bottom line is I can testify NNs work quite well for that purpose. SVM is slightly better though, in my limited experience.

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I think it's indeed all about data, but there's no end to what you could feed it with in order to be more accurate : winning/loosing streaks, players biorhythms, player's girlfriends mood before the game, minor/major injuries they suffered in the recent past, extra-sportive events that are bothering the players, etc, etc, etc.

But I don't think you can accurately predict which team is more likely to win, it would be just a more-or-less educated guess.

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In my opinion and experience, because of the excessively large number of factors in play, designing and training the ANN will be unreasonably complex and time-consuming. ANNs are good at pattern matching, and game prediction takes much deductive reasoning rather than mere pattern matching.

But if you want to enjoy learning neural networks, it will be a good adventure. If you are successful, you might want to host your code somewhere for others to see and learn!

For game prediction, it would be much easier and faster with decision trees or a rules engine and so on. This will be no easy task either, but it will be another interesting activity.

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My belief is that the unpredictability of an event is due to lack of information and understanding...If you have all the knowledge, then yes it could be done. Or, the more knowledge you have, the better it can be done.

So in theory, the answer is yes.

However, in practice, you can get a PhD and have a whole career working on this question and you still may not succeed.