We use our predictive model to forecast unplayed tennis matches
Update 1/27/2017 6:05 pm
Final Predictions
| Winning Player | Losing Player | W-L | % |
| Roger Federer (17) | Rafael Nadal (9) | 11-23 | 73% |
SF Results
2/2 matches correct
Update 1/25/17 6:45 pm
SF Predictions
| Winning Player (rank) | Lossing Player (rank) | W-L | % |
| Roger Federer (17) | Stanislas Wawrinka (4) | 18-3 | 83% |
| Rafael Nadal (9) | Grigor Dimitrov (15) | 7-1 | 62% |
QF Results
2/4 matches correct.
Creating an algorithm for tennis match forecasting
So far, we’ve covered general descriptive statistics, such as yearly attribute trends, points won distributions, and competitiveness on our blog. We build on those findings by creating a model capable of predicting ATP tennis match winners. Using historical data points, we achieve 81% accuracy in predicting match winners for the 2016 Australian Open. We delve into the development process and share our predictions below.
Continue reading “Predicting the Winner: 2016 Australian Open”
BigTime Stats 