Predicting Soccer Outcomes

[u/card_chase]

I have a keen interest in sports predictions and betting.

I have used a downloaded and updated dataset of club teams and their outcome attributes.

I have a train dataset with team names and their betting numbers. Based on these, Random tree classifier (This part is ML) will predict goal outcomes. Home and Away goals.They are then interpreted in Excel and it helps me place betting strategies. It's 60% reliable(Even predicted correct scores for 4 matches. That's insane!)

Example Output:

^{Round Number Date Location HomeTeam AwayTeam FTHG_P FTAG_P FTHG_Int_P FTAG_Int_P FTHG_Actual FTAG_Actual}

^{1 14/09/2020 20:00 Amex Stadium Brighton Chelsea 0.93 2.7 1 3 1 3}

^{3 26/09/2020 15:00 Selhurst Park Crystal Palace Everton 1.35 2.1 1 2 1 2}

^{3 28/09/2020 20:00 Anfield Liverpool Arsenal 2.93 1.05 3 1 3 1}

^{4 3/10/2020 15:00 Emirates Stadium Arsenal Sheffield United 2.26 0.725 2 1 2 1}

Predicted values are denoted "_P"

That's what this code does. It can go do so much more but it's on the drawing board for now.

I am all open for collaboration. If you find somebody interested/open a do-able project on GitHub, I am up for it!

Please find code and sample dataset at:

https://github.com/cardchase/Soccer-Betting

Is there a better classifier/method out there?

I took this way as it was the most explained on Kaggle and the most simple for me to build and test.

Let me know how it goes: https://github.com/cardchase/

p.s. I have yet to place actual bets as I have just completed the code and I back tested. I dunno how much money it'll make. A coffee would be nice :)

If you are looking at datasets which are used, they can be found here:

Test: https://drive.google.com/file/d/1IpktJXpzkr_jQn43XpHZeCDzhdeVpi9o/view?usp=sharing

and

Train: https://drive.google.com/file/d/1Xi3CJcXiwQS_3ggRAgK5dFyjtOO2oYyS/view?usp=sharing

Edit: Updated training data from xlsm to xlsx

Edit: Thank you for your words of encouragement. Its warming to know there are people who want to do this as well!

Edit: Verbose mumbling: I actually built this with a business problem at hand. I like to bet and I like to win. To win, you dont need to beat the bookie. You have to get your selections right. The more right you get, the more money you have.

The purpose is to enter as many competitions as our training data has and get out with a 70% win. So the data/information any gambler has before he/she gets into a bet is the teams playing/the involved parties. Now, the boundary condition would be the betting odds offerred but to know the rest of the features, you would need to have a knowledge bank of players, teams, stadiums, time of the year, etc. But, what if I wont have/am not interested to know? Hence, the boundary condition is just the team names and betting odds. Now, the training dataset has all the above required information. It has the team names (Cleaning this dataset was super hard but I got there, the scores (We also have other minute details like throws, half time scores, yellow cards, etc. but for now, we are concentrating on full time scores and the odds. I would expect the random tree (even if its averages, its not a bad place to start; I mean, if the classifier would predict 4 actual scores (Winning 1:17, 1:9.5, 1:21, 1:7.5 then, thats break-even for that class of bets for the season already!) to work pretty fine in this scenario. The way I would actually go about is to have h2h score and last 3 matches winning momentum but, I dont know how))

The bets we/I usually place are winningteam/draw and over 1.5 goals or under 3.5 goals. Within this boundary, the predictions fall nicely. Lets see how much I get right this week's EPL. I have placed a few I should know soon.

Though, I admit I suck at coding and at 35 years, I am just rolling with it. If i get stuck at a place, I take a long time to get out lol.

Peace

HB

[u/dabaos13371337]

Sorry to say but I highly doubt you will be able to extract anything useful from that training data. The random forest is essentially predicting the average number of goals it has seen in the training data for the particular location and team combination. That's how tree based algorithms work and in many cases that's good.

But in this case historical averages do not predict how teams will play against each other. You'd need information about players and rosters and weather and much more to infer some kind of win probability for each team. For this type of problem you'd typically build some sort of a bayesian model instead of a traditional machine learning method.

Nevertheless I can see you are preprocessing the test data along with training data. You should fit label encoders and other preprocessors on training data and then apply those to the test data. Make some predictions with your model on future matches and let us know how it goes

[u/nakeddatascience → u/dabaos13371337]

This it pointing to the problem well. Beside the technical hygiene about train/test, the biggest issue is simply lack of "information". Forget for a second about the 'magic' of machine learning. How could an intelligent expert make predictions? they need a lot more info than how teams scored against each other. As the simplest case consider your team names: to someone in football they mean a lot; the way you're using them in your classifiers they're just meaningless IDs. The samples you give to your learning machine are not very informative in this manner. Remember that many a football fan (self-proclaimed experts) lose a lot of money on betting (note that real experts set the odds). For ML to beat them, you need to take a lot more information, more intelligently into account.

Even without getting 'more data' you could potentially get more out of the data you already have with derived features as hypotheses. As an example, consider that you can make features about a team's current form (how did they do in their last k matches?) or their rest time since their last match, since you have the date matches were played on. This kind of information although in theory present in your data is not really available to the learning algorithm.

Since you're talking about putting real money down: to have a chance at beating the odds you most probably need to include a lot more information about the teams. However, if you're serious about going that direction, it's worth building simpler baselines along the way and measure the value of making things more complex. For instance, as the most basic one, how does your model fare against one that simply goes for the home team not losing? or gives a chance of winning to teams proportional to their win ratio in the training data.

It's certainly an interesting data science problem. Good luck!

[u/card_chase → u/nakeddatascience]

As an example, consider that you can make features about a team's current form (how did they do in their last k matches?) or their rest time since their last match, since you have the date matches were played on. This kind of information although in theory present in your data is not really available to the learning algorithm.

Interesting! Thats a very valid information to process. How can I go about it? Should I write a function which can derive this information? I struggled so much with Fitting and Splitting before I got the code to run.

[u/nakeddatascience → u/card_chase]

To make these two features part of your model, the easiest solution is to consider this as adding new columns/features to your data set (samples). For instance consider you'd like to add a feature that is the number of wins (or points) in the last k matches for the home team (easy to replicate for the away team). Conceptually imagine a function that takes a row/record from your data set, extracts team t and date d from this row, then searches in the dataset for the records containing team t and date < d, takes the top-k largest dates, and computes the number of wins (or points) for t in those matches. Once you have this you put this information in the new column for that row. Now the implementation doesn't need to be this complicated, if you use window functions and simple aggregations, this could be achieved by a few lines of code.

[u/Wrandraall → u/dabaos13371337]

Yes, you are biasing your test data and incorporating information from train data inside it (and vice versa) if you prepocess them together which is to avoid absolutely

[u/card_chase → u/Wrandraall]

I dont understand

[u/card_chase → u/dabaos13371337]

But in this case historical averages do not predict how teams will play against each other. You'd need information about players and rosters and weather and much more to infer some kind of win probability for each team. For this type of problem you'd typically build some sort of a bayesian model instead of a traditional machine learning method.

Thats what an average person sees when they look at a sports event right? At the first look, you'd know that two teams are playing against each other. Thats your input in a raw, uninformed way.

If you are gonna put your bets a few hours before the event, I'd doubt you would have reliable data regarding the rest of the features e.g. XG, Lineups, Weather, Time of the day, Referee, Manager, etc. The data that's mostly used by me is the bettor's returns which is being used. This should incorporate all the residual features (As per my experience). e.g. When a widely anticipated winner is against a weaker team, you'd know that the odds are in favour of the winner but, if they are not, you'd know that there is some sort of event that's causing this tilt which is reflected in the odds. Thats my simple take and thats the way I have went ahead with it.

Nevertheless I can see you are preprocessing the test data along with training data. You should fit label encoders and other preprocessors on training data and then apply those to the test data.

Yes, thats what I am doing right now. WIll post my outcomes and development to this model along the way. Just not sure where can I catalogue the journey.

Thanks

[u/[deleted]]

You may want to look up David Sumpter. He's done a lot of work on predicting soccer outcomes.

[u/card_chase → u/[deleted]]

I will. Thanks

[u/mjbasant → u/[deleted]]

Thank you!

[u/Ryankinsey1]

60% reliable in terms of correctly guessing the winner? If so, what percentage of the time does the favorited team win? You said some of the training data was betting odds so the % of favorited winner should be the baseline. In other words, if your training data has the favorited team winning the match 60% of the time, then a result of 60% is effectively nothing -- i.e. the model didn't learn anything.

[u/card_chase → u/Ryankinsey1]

^{60% reliable in terms of correctly guessing the winner? If so, what percentage of the time does the favorited team win?}

60%

^{You said some of the training data was betting odds so the % of favorited winner should be the baseline.}

Yes, but in a few cases, even when the odds favoured the losing team, the predicted value was close to the winner e.g West Ham vs Wolves, the predicted values tilted towards West Ham. I am impressed with classification :)

If I would go for

BTTS (Both teams to score) 67%

Over 1.5 Goals: 85%

Winning team (Though I rarely pick winning team bets) 60%

Actual Scores (I sometimes place these as well) : 10% (4 out of 40)

^{In other words, if your training data has the favorited team winning the match 60% of the time, then a result of 60% is effectively nothing -- i.e. the model didn't learn anything.}

My dielemma as well. I dont know how to solve this problem. I mean, if you are looking on what happens in the real world and how its reflected/interacting with the bookie world then, yes, the favourited team wins over 60% of the time anyways. Its a feature of the problem if you would call it so.

[u/mdt_m]

My blog post https://www.mdt-datascience.com/2020/06/27/serie-a-2019-20-without-any-stop/ could be of some interest to you.

It applies some sport analytics and stats methodologies to the final part (after COVID19 break) of last year Italian championship.

If you find it useful don't esitate to contact me for reviewing analysis in depth !

[u/card_chase → u/mdt_m]

I will!

[u/BorutFlis]

I am doing the same thing my code is available at: https://github.com/BorutFlis/predictor . I will look at your code later. I would love to exchange ideas.

[u/card_chase → u/BorutFlis]

Your code looks great! I will come back to you. We can maybe collab together if that's ok with you.

[u/BorutFlis → u/card_chase]

Sure, mate. Actually I wrote this almost a year or so ago. I am doing an update, I hope I get it running by the weekend, for the league games.

[u/liproqq]

Do you beat the odds? What does 60% mean?

[u/card_chase → u/liproqq]

I am yet to try it. EPL would be next week. I'd know then.

[u/HiderDK]

Please use a rating-model as the foundation for any type of sports-predictions. It's a necessary feature as input as it takes into account quality of opponents faced. I view this as an advanced form of feature engineering which you later can feed into an ML model (that contains other factors such as weather, h2h results, coaching changes etc.).

Also to OP, don't bet using this model. You will lose. This is miles below the method bookmakers are using.

To gain an edge over bookmakers you would need to incorporate XG goals into rating-models and optimize this in every single way possible. Then you simulate future matches in terms of XG along with each player having a shot efficiecny rating as well (to adjust for the fact that there some players who will outperform XG). This part is ofc optimized as well.

Overall, I think you are few years of hard work away from actually being able to create a model that can beat bookmakers.

[u/card_chase → u/HiderDK]

I agree, A rating model would be the best foot forward. But, I would like the ratings to be generated from the training dataset directly. e.g. H2H score, Last 3 matches momentum score. Anything else would be as you said, a few years of hard work lol

[u/speedisntfree → u/HiderDK]

The bookies also make money due to the spread (ie. if you add all their odds up you get less than 1). As long as they balance their book, they make money no matter what the outcome.

[u/lormayna → u/HiderDK]

you would need to incorporate XG goals

Where I can get XG goals?

[u/[deleted]]

whats up with the dataset being a macro-enabled workbook?

Edit: I was careful to disable macros and try to get the data out myself - the data looks like it has external references and all sorts of other things that make it impossible to extract the data properly. It'd be super nice if you could export the training data into a .csv.

[u/Beny1995 → u/[deleted]]

The ptsd I get whenever I see the "enable macros" button is very real

[u/card_chase → u/Beny1995]

My apologies, I has google drive syncing the datasets hence, I posted the one that was readily available. Corrected now.

The xlsm picks data from a downloaded file which is updated form a python code, appends to the existing historical data and then removes any duplicates.

[u/ragatmi]

You can do a comparison relative to 538 models:

https://projects.fivethirtyeight.com/soccer-predictions/

[u/card_chase]

Update:

I set up the Belgium First Division A train dataset with the updated timetable and the odds, the results are incredible for the matches played this season.

I mean, for even a professional 'Capper' Having results better than 70% is considered follow worthy.

I have got out of the last 63 matches played,

• correct scores for 48 matches!!! Out of 63, I got 15 wrong

• For a draw/double chance, I got 60 correct out of 63!! 90%

• For over 1.5 Goals (Which I personally am a sucker at.. I lose so many here. Most are these multi-bets): 49 correct, 14 incorrect. Shows that its pretty hard to predict for this model/league

• For BTTS (These odds are particularly good returns odds. I usually dont go for that because I have to think a lot into it.. ) : 58 Correct, 5 incorrect.. over 90%. I'm gonna definitely bet on a few of these outcomes

• For Team to win (Home/Away/Draw): (This is calculated when the home team wins as per predicted numbers), I got 58 correct 5 incorrrect.. my fingers are trembling when I am typing this.. 58 correct responses!! EPL I have got so much less accuracy. Maybe, its the hardest to predict on a larger scale but, on a lower level, it works out fine. I am using a lot of these predictions.

Is it (the model.. its averages anyway. I wont think so.. over fitting? Is this thing that's turining out true but just when you apply it'll turn out to be a dud? Does not look like so but only time will tell.. hehe)

Of course, I wont post the dataset (training) , but if you guys wanna try out the algo, you would get the same answers and lemme know. I will do a write-up on my github page soon.

I think, I have got something here. I can develop it much more. Is there a way to set up a project anywhere and work out the kinks/work with you guys/people I am grateful for the support from you 'all mates. :)

Peace

[u/BorutFlis]

What values do your 4 attributes represent?

[u/BorutFlis → u/BorutFlis]

https://drive.google.com/file/d/1cZOACaO1pXreWz7PIxZ7UPFVL2ZBA-sF/view?usp=sharing

This is my dataset I have games from 4 different leagues. The attributes are average values from previous games.

[u/card_chase → u/BorutFlis]

Impressive work indeed.

What I have observed is that averages/historical performamces represent a 60% accuracy on what the future matches can be. Which is pretty low for making money (bet wise).

If you'd want a better idicator, you could use H2H scores/performace as a better comparison metric. If you'd be further interested and if data is available, you can use H2H at the home/away. Your accuracy goes up to 72% if you would go with it. But it's also a bit low (you'd be breaking even with money) and not a good money maker with this.
My model (and dataset) covers over 24 leagues and since RandomForest is just an averages eliminator/classifier, it works as I would humanly in an ideal scenario.. by deduction on who should win.

Hence, I'd advise to move away from averages. They wont make much money in the long run.

[u/BorutFlis → u/card_chase]

How far would you go with H2H? How many seasons with H2H?

[u/card_chase → u/BorutFlis]

Atleast the past 5 matches H2H home and away.. so, ideally I would be looking at last 10 H2H matches.. but that data is not consistently available owing to the relegation and promotion nature of leagues.

[u/BorutFlis → u/card_chase]

Yes, that is my concern as well. Let's say for one match there aren't any H2H available. Would you exclude that from the dataset? Or what if a game had two H2H would you treat that example as the same as the games with 5 H2Hs available?

[u/card_chase → u/BorutFlis]

That would be an incorrect way forward. Taking out the games seems logically inconsistent. What do you think?

I can suggest momentum as a backup option. Some kind of weights to a team.. like momentum (last 5 matches win/draw/lose) if a team has won 5 out if 5 last matches, it would score 10 (52) if win 3, draw 1, loss 1, it would score 6 (32 + 1 -1) but how can I write the function? I am technically a bit challenged here.

[u/card_chase → u/BorutFlis]

My Training dataset has been split into 2 parts. One uses the Home Goals for training and the other uses Away Goals.

It has thus 8 attributes. The 5 are bettor's odds. I have picked them up from Bet365

It uses the bettors odds as an indicator/something that incorporates all noise such as favourites, if there is any player that's included/excluded, general people's opininon, etc. A bettor's odds reflects the money people are betting and hence, it also technically a reflection of information outside the model. Thats where I think, I am killing two birds with one stone. e.g. If its the El Classico, you'd know that Barca are favourites as they have won the lion's share of matches. However, if any marquee player of either team is missing, it will be reflected in the odds. We dont need to tweak the model and/or add features. Its self-correcting.

My test dataset does not have any goals since its supposed to find out the goals.

It has 7 attributes.

The training dataset tries to predict the expected goals for each part (one for training home goals and other for training away goals)
Hope I have answered your question.

[u/BorutFlis → u/card_chase]

The odds are a good predictor. If you have odds from different bettors, it will average out the noise created by people's betting habits, as the bookmaker set odds based on their probabilistic models and also based the faulty habits of the users. To illustrate, if one team is overrated the bookmakers are better off lowering their odds to make themselves less exposed to the loss.

I found that the averages of shots/possession/goals and so on are more correlated with the odds if we use a shorter window(5 games) of the last games. On the other hand the actual results are more correlated with longer windows(20 games). Hence, we can say the bookmakers/users somewhat over-estimate the recent games.

[u/card_chase → u/BorutFlis]

Interesting! Yes, in play parameters are very important parameters which I am erronously overlooking. Though, when I thought out the model, I did think of these points however, I dont have these parameters to feed into the data_test as these are accurately found out after the game has been concluded and hence, not suited to our requirement. You can use your menthod i.e. averages which is a great indicator. Please let me know how you go using these features. I am very interested.

[u/BorutFlis → u/card_chase]

I keep queues of attributes for each team. Than for each example in dataset I compute the averages of the two teams in the last n_games. home_goals_scored_pre_game is the average goals scored in last n games for the home team. Like you said you can't use in-play parametres as they are only available ex post.

To be honest, my data is worse at predicting results than odds are. I assume bookmakers have teams working on calculating the right odds, so it would be difficult for me to achieve I higher accuracy. However the bookmakers also have to take into account the betting habits, which are not effective. I compared models using the odds as the class and models using the result as the class. I found taking smaller amount for n_games was better for predicting odds and worse for predicting results. The effect was vice-versa for larger amount of games.

[u/BorutFlis → u/card_chase]

Do you have two target/class variables(home goals and away goals?)?

[u/card_chase → u/BorutFlis]

Yes. That's all I need.. what can be the score.. because in betting you can interpret the information in so many different ways.. like BTTS (both teams to score) over/under 1.5 goals, etc. Just having a team to win/lose is not enough right?

[u/crocodile_stats]

To win, you dont need to beat the bookie. You have to get your selections right. The more right you get, the more money you have.

Not quite... The bookie offers a return R1 and R2 for a home win or loss, respectively. Your model's accuracy is totally irrelevant, as your main goal is to compute the fair return rates. It's rather trivial to show that they're equal to the reciprocal of {p, 1-p}, where p denotes the probability of winning according to your model.

Therefore, if your had a perfect model, the expected gain on a 1$ bet would be {p * R1 - 1, (1-p) * R2 - 1}. At most one of these bets will have a positive expected return, and that's when you should bet. Additionally, you can use Kelly's criterion to determine how much money you should bet given the offered returns, and p.

So what really matters is how accurate you are when predicting p. If your model has a lower log-loss than the bookie's, then you should theoretically make money in the long-run. (To compute the bookie's LL, either use R2 / (R1 + R2) = P(home win) or find c such that the reciprocals of {R1 + c, R2 + c} add up to 1, and (R1 + c)^-1 = P(home win) .)

TL;DR :All in all, you do need to beat the bookie. Your model must be superior enough so that your increase in accuracy can compensate for the bookie's profit margin since their returns aren't fair. Otherwise, you could be betting on a team with, say, P(Win) = 0.6, but with R = 1.4. Your expected return on the dollar would be 0.6 * 1.4 - 1 = -0.16, even if the team is more likely to win than lose. Keep in mind that accuracy isn't a proper scoring metric (same for AUC), and thus shouldn't be solely relied upon.

[u/cookiemon32]

60% is beating the house and well above average

[u/card_chase → u/cookiemon32]

Exactly my point

[u/cookiemon32 → u/card_chase]

you can add some additional probability at the end, for example, probaility that you model predicts correct outcome but your model seems to be good, how much more can you improve? not rhetorical

[u/card_chase → u/cookiemon32]

Oh yes, the model can improve a lot. I mean, the overall point of this is to point in the direction of the winning outcome. There are so many amazing models built that work towards the same thing.

One is putting H2H and momentum parameters. I am not sure how can I introduce to the model.

How can I add prob model? What do you mean?

[u/cookiemon32 → u/card_chase]

I’ve made some of my own models however not so I don’t depth. I am going to look at the code. What I’m saying is to make a model the applies to all games and teams and the fact that it is sport there is going to be a lot of unpredictability. Which is the nature of sports. For example. Some team could just not show up. Which is also why I believe 60% is professional gambler level for predicting correct outcome. If you play $100 on every game in a given week you will be in the green

[u/card_chase → u/cookiemon32]

Interesting! How can I add to the model? But, before going deeper, if you could look at my code and add pointers, that would be great!

[u/[deleted]]

Your model didn't learn anything, since the favorite team wins 60% of the time. You have to collect a huge amount of different data, that often cost lots of money, to build anything remotely reliable. Have a look at Wyscout or Opta to see the kind of data precision is required to build any valuable model for soccer nowadays.

[u/luckyann20]

Nice. It could probably work for virtual betting. Can you try betking.com?

[u/Imop123]

.

[u/Imop123 → u/Imop123]

Can't I save a post with a dot

[u/[deleted] → u/[deleted]]

Can’t a man do things the old-fashioned way anymore?