We use our predictive model to forecast unplayed tennis matches
Update 1/27/2017 6:05 pm
|Roger Federer (17)
||Rafael Nadal (9)
2/2 matches correct
Update 1/25/17 6:45 pm
|Winning Player (rank)
||Lossing Player (rank)
|Roger Federer (17)
||Stanislas Wawrinka (4)
|Rafael Nadal (9)
||Grigor Dimitrov (15)
2/4 matches correct.
Continue reading “2017 Australian Open Predictions”
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”
We compare points, games, sets and other statistics from 1968 to today in order to answer how tennis has evolved.
One of the first things to consider when assessing the competitiveness of any sport is how many points are scored by opposing players and teams. In basketball, for example, a score line of 101-99 is way more competitive than 130-70, even though the total points scored is the same. In tennis, results are dictated by a scoring hierarchy (point-game-set-match), therefore, there are many ways to judge the competiveness of a match. Tracking each of these scoring numbers over the years reveals just how much the competition has evolved – or stayed the same…
We first started at the micro level Continue reading “How competitive is men’s tennis?”
Identifying statistical trends in the men’s tennis game
As part of our data exploration series, we used available datasets, as well as our data to map an increasing trend in height and aces, and a decreasing trend in double faults since 1991.
Continue reading “Interactive: Tennis Stats, Height, and Age trends on the ATP World Tour”
We answer how court surface impacts height and serving statistics
Thank you for visiting TenniStats. We are a blog that focuses on the analysis and visualization of tennis data. In this first post, we start by exploring our dataset and sharing some general insights to lay the groundwork for future discussion. Please subscribe to stay up-to-date on our progress!
We first examined data for the 2015 season consisting of every men’s singles match played at the ATP 250 level or higher. It contains data points on the tournament, players, and statistics of each match, including points won, aces, and double faults. We examined how court surface impacts these match statistics, as well as how height may give an advantage on different surfaces.
Continue reading “Data Exploration: Surface, Serving, and Stature, by the Numbers”