AVP & FIVB Beach Volleyball Match Database

New match database that contains top players such as Phil Dalhausser, Nick Lucena, Kerri Walsh, and April Ross

Welcome to BigTimeStats, the TenniStats rebrand, now with beach volleyball match data. Drawing inspiration from Jeff Sackmann’s tennis match database, I’ve compiled all AVP and FIVB matches going back to year 2000, available as open source csv/excel files on Github. Continue reading “AVP & FIVB Beach Volleyball Match Database”

2017 Indian Wells Forecasts

Blogging from the bush, we’re bringing you our latest forecasts

We are on site in the California desert, bringing you our predictions for the 2017 Indian Wells BNP Paribas Open.

Updated 3/19/2017 1:45 pm ET

Final

Winning Player (rank) Losing Player (rank) W-L %
Roger Federer (10) Stanislas Wawrinka (3) 19-3 72%

Updated 3/18/2017 2 am ET

SF

Winning Player (rank) Losing Player (rank) W-L %
Stanislas Wawrinka (3) Pablo Carreno Busta (23) 2-0 76%
Roger Federer (10) Jack Sock (18) 2-0 83%

Continue reading “2017 Indian Wells Forecasts”

2017 Australian Open Predictions

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.

Continue reading “2017 Australian Open Predictions”

Predicting the Winner: 2016 Australian Open

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”

How competitive is men’s tennis?

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…

By Points

share-of-points-won

We first started at the micro level Continue reading “How competitive is men’s tennis?”

Interactive: Tennis Stats, Height, and Age trends on the ATP World Tour

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.

Story2Pic1

Continue reading “Interactive: Tennis Stats, Height, and Age trends on the ATP World Tour”

Data Exploration: Surface, Serving, and Stature, by the Numbers

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!

Picture1 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”