This repo contains point-by-point data for most main-draw singles Grand Slam matches since 2011. It was scraped from the four Grand Slam websites shortly after each event. There are two files for each tournament. "-matches.csv" contain metadata for all the singles matches included from the tournament, and '-points.csv' contains all the available data for each point.
Point-by-point stats are so much more revealing: They show us how matches unfold and allow us to look much deeper into topics such as momentum and situational skill. These are subjects that remain mysteries–or, at the very least, poorly quantified–in tennis.
tennis_pointbypoint. Sequential point-by-point data for tens of thousands of pro matches. Each row in these files represents one match, and contains the following: date; tournament name; tour (ATP, CH[allenger], FU[tures], WTA, or ITF [women]) draw (Main [draw] or Qual[ifying]) server1 (the player who served first) server2
For instance, if a player wins a point, his chances of winning should go up. If a player loses a game, his chances of winning should most definitely go down. (2) While I had six years (2010-2015) worth of point by point tennis data, it actually is not enough to use it as a source of truth. A tennis match contains thousands of different situations, and after splitting the point-by-point data into these various states, each match situation on average only contained fewer than 100 observations.
Livesport.com offers scores service from more than 3000 tennis competitions from around the world - ATP tournaments, WTA tour, challengers, ITF tournaments and also team competitions - Davis Cup and Fed Cup. Follow ATP and WTA matches point by point! You will find the "Point by point” tab with highlighted lost serves, break points, set- and match points in match details of all ATP and WTA matches.
Tournament GOAT Points tab to check who earned them the most on the particular tournament: Australian Open, Roland Garros, Wimbledon, US Open; Only for computer geeks, Docker image mcekovic/uts-database with PostgreSQL database pre-populated with ATP tennis data as of season 2020 (more info at #337)
I’ve just released point-by-point data for most Grand Slam singles matches back to 2011. Beyond the basic point sequence–which is valuable in and of itself–you’ll find serve speed, winner type, and for a few of the slams, rally length for each point. More detailed notes on the data are available at that link.
Probability of Winning at Tennis I. Theory and Data By Paul K. Newton and Joseph B. Keller The probability of winning a game, a set, and a match in tennis are computed, based on each player’s probability of winning a point on serve, which we assume are independent identically distributed (iid) random variables. Both
This point-by-point data is easily the most important data source. Unfortunately the Grand-Slam organizers decided to keep it private (even though it's publicly observable, i.e. they don't have a "copyright" or anything on that data - it would just be incredibly tedious to go back and rewatch all the GS matches).