This is a linear regression model with an additional term as a penalty.

Due to the multicollinearity between the independent variables, the traditional linear regression does not give stable results, we use the target_difference function as the target variable. We print the coefficients of the model. The result looks like this: these probabilities for each team can be considered the qualification for each team. The higher the coefficient / rating, the stronger the team.

By this model, the Colorado Avalanche is the highest and highest rated team. My favorite Toronto Maple Leafs team is also recognized as a good team model! They did it! There are a few more things to consider before applying this algorithm to sports betting. The statistical method appears to be more complex than traditional methods. But how do you compare the services? Let’s take a look at three other traditional methods.

As we discussed in the previous section of this article, these are basic stats that appear frequently on sports sites. For each specific team,% Win / Loss = Total Games Won / Total Games Played. As the name suggests, this is a bet to always choose the home team to win. This is a sophisticated method that also includes information on goal difference and home advantage. However, when forming the team rating, the strength of the team’s opponents is not taken into account. The ridge regression method takes this into account, as it looks at all teams and all games together.

Skip this if you hate formulas.

First, we calculate for each individual team: team goal difference per game = (team goals – team goals) / (team games played) Then we use all the results of previous games to get the statistics, taking into The team’s home advantage counts: Home advantage Goal difference = (goals scored by all home teams – goals by all visiting teams) / (matches by all teams) With this statistic we can predict whether the home team or the visiting team will win a given match. Use the example from the beginning again. Team A (home team) plays against team C (away team).

We use the following statistics to predict the outcome: Margin = team A goal difference per game – team C goal difference per game + home advantage goal difference. If the margin is> 0, we bet on the victory of team A (home team). If Margin <0, we choose team C (guest team).

To compare these methods, we use cross-validation to estimate.

Our statistical model is a winner! He was 60% accurate in predicting the outcomes of hockey games! However, in the early stages of the season, it is better to rely on other metrics. Because the model output only gets better and better than other methods during the season (when more data is available). Of course, there is still room for improvement in our forecasts. You can add variables based on the current script. Did the team play or rest in the last few days?

How much did the team travel outside of their hometown? The situation in the team always changes during the season. Therefore, the latest games should be more informative than the previous ones. Adding an indicator for this will help. We use Ridge’s regression model as an example. However, for best results, you can try and combine other statistical / machine learning models such as neural networks (GBM). Models may not contain all information. As a seasoned sports fan, you must have valuable knowledge.

The combination of statistical techniques and your experience is essential to make the best predictions. Sports betting is a great way to practice data science while having fun. Fit the model before the chip drops! Good luck to you all! Thank you for reading. I hope this sports betting guide has been helpful to you. Now I would like to hear what you have to say. Feel free to leave comments below. More Data Science Articles from Lyanna and Justin – Written Case Studies, Research, Tutorials, and Best Practices Monday through Thursday. Make studying your daily ritual.