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An analysis of point by point data –

I’m a big fan of Tennis. When I’m not working in the university, you can probably find me in my favourite tennis club, Sogipa. What is so great about Tennis? It is a sport that challenges your physically and mentally. It forces you to your limit in both aspects. The more you play, the more you love it and the more you want to learn about the game.

Tennis is also very different from the university work, which is mainly a brain effort of writing, speaking and coding. I find it very healthy to be able to momentarily distance myself from the day to day work, even for a few hours. As my close friends can testify, I won’t shut up about tennis and try to convince other people to start playing. Now, let’s get to part of this blog that matters.

In this post I will combine two things that I love, R and Tennis. As you will see soon enough, you can find a large database of information about tennis matches publicly available in the internet. In my first post in this topic, I will describe the evolution and effects in the game over time, specially a change in the equipment back in 2000.

## The data

The data gathered for this post is from the great repository of Jeff Sackmann. There you can find csv files with information from ATP matches, WTA and more. Among the repository choices, the most impressive is the Match Charting Project. It contains point by point data for thousands of matches. Yes, that’s right, all points of the match are registered following particular guidelines. This is a collective effort, where the public can send statistics to the project. The level of details is amazing, you can simulate the whole match based on the csv files.

Now, lets get some code going. Our first step in this analysis is to download and unzip the dataset.

zip.file <- 'TennisData.zip'

if (!file.exists(zip.file)){

}

unzip(zip.file)


Let’s look at the contents of the folder from the zip file.

list.files(path = 'tennis_MatchChartingProject-master/')

##  [1] "charting-m-matches.csv"
##  [2] "charting-m-points.csv"
##  [3] "charting-m-stats-KeyPointsReturn.csv"
##  [4] "charting-m-stats-KeyPointsServe.csv"
##  [5] "charting-m-stats-NetPoints.csv"
##  [6] "charting-m-stats-Overview.csv"
##  [7] "charting-m-stats-Rally.csv"
##  [8] "charting-m-stats-ReturnDepth.csv"
##  [9] "charting-m-stats-ReturnOutcomes.csv"
## [10] "charting-m-stats-ServeBasics.csv"
## [11] "charting-m-stats-ServeDirection.csv"
## [12] "charting-m-stats-ServeInfluence.csv"
## [13] "charting-m-stats-ShotDir.csv"
## [14] "charting-m-stats-ShotDirection.csv"
## [15] "charting-m-stats-ShotDirOutcomes.csv"
## [16] "charting-m-stats-ShotTypes.csv"
## [17] "charting-m-stats-SnV.csv"
## [18] "charting-m-stats-SvBreakSplit.csv"
## [19] "charting-m-stats-SvBreakTotal.csv"
## [20] "charting-w-matches.csv"
## [21] "charting-w-points.csv"
## [22] "charting-w-stats-KeyPointsReturn.csv"
## [23] "charting-w-stats-KeyPointsServe.csv"
## [24] "charting-w-stats-NetPoints.csv"
## [25] "charting-w-stats-Overview.csv"
## [26] "charting-w-stats-Rally.csv"
## [27] "charting-w-stats-ReturnDepth.csv"
## [28] "charting-w-stats-ReturnOutcomes.csv"
## [29] "charting-w-stats-ServeBasics.csv"
## [30] "charting-w-stats-ServeDirection.csv"
## [31] "charting-w-stats-ServeInfluence.csv"
## [32] "charting-w-stats-ShotDirection.csv"
## [33] "charting-w-stats-ShotDirOutcomes.csv"
## [34] "charting-w-stats-ShotTypes.csv"
## [35] "charting-w-stats-SnV.csv"
## [36] "charting-w-stats-SvBreakSplit.csv"
## [37] "charting-w-stats-SvBreakTotal.csv"
## [38] "charting-w-ufe-fe.csv"
## [39] "MatchChart 0.1.5.xlsm"


That’s a lot of files! But, once you read the manual, the structure is quite simple. The m and w in each file means men (masculine) and women (feminine) tennis matches. The files with stats in their names are the product of a particular aspect of the game. All analysis that resulted in these files are based on the master files charting-m-points.csv and charting-f-points.csv.

These are the largest files from the repository, containing point by point data. For example, the first row of column 1st in charting-m-points.csv shows the code 6b29f3f3f3b3f1f2f3b3u1*. The first letter indicates the first serve. The value 6 means a serve down the T, that is, a serve in the “T” located in the middle of the court. Each following shot is represented by a number that indicates direction and depth (e.g. 4=out wide) and a letter that determines shot type (e.g. f=forehand).

The game keeps moving forward and each pair of number and letter sets the configuration of shots from the players, back and forth. Eventually, the point ends when someone misses or hits a winner. For the example code 6b29f3f3f3b3f1f2f3b3u1*, the last symbol * indicates that someone has hit a winner and that event ended the point. As you can see, this is an extremely detailed registration of a match. We only scratched the surface, there are many more details about the coding system in file MatchChart 0.1.5.xlsm.

Our first analysis will be to look into the volume of registered games over time. We will combine men and women data in the analysis.

library(dplyr)
library(ggplot2)
library(stringr)

# files
my.f.m <- 'tennis_MatchChartingProject-master/charting-m-matches.csv'
my.f.w <- 'tennis_MatchChartingProject-master/charting-w-matches.csv'

my.cols <- cols(
match_id = col_character(),
Player 1 = col_character(),
Player 2 = col_character(),
Pl 1 hand = col_character(),
Pl 2 hand = col_character(),
Gender = col_character(),
Date = col_character(),
Tournament = col_character(),
Round = col_character(),
Time = col_character(),
Court = col_character(),
Surface = col_character(),
Umpire = col_character(),
Best of = col_integer(),
Final TB? = col_integer(),
Charted by = col_character()
)

my.df.matches <- bind_rows(read_csv(my.f.m, col_types = my.cols) %>% mutate(Gender = 'Men'),
read_csv(my.f.w, col_types = my.cols) %>% mutate(Gender = 'Women'))

# create year col
my.df.matches$Date <- as.Date(my.df.matches$Date,'%Y%m%d')
my.df.matches$Year <- format(my.df.matches$Date,'%Y')

# plot it!
p <- my.df.matches %>%
group_by(Year,Gender) %>%
summarise(Number of matches = n()) %>%
ggplot(aes(x=as.numeric(Year),y=Number of matches,color=factor(Gender))) +
geom_line(size=2)

print(p)


As you can see, most of the data was registered for matches after 2010. Interestingly, the number of men and women games follow a very similar pattern. The drop in 2016 can be explained by the registration process. It is likely that people watch recorded matches in order to fill out the points. I would predict that the drop will be lower once the data is again downloaded from the repository in 2017.

Now, lets look at the distribution of court types. First, as with any open field in a database, there are lots of problems with the court type field. If we just count the cases, we get:

temp.tab <- table(my.df.matches$Surface) print(temp.tab) ## ## Carpet Carpet (hard) Clay Court des Princes ## 3 1 583 1 ## Grass hard Hard Hard Court ## 259 1 1410 1 ## Hard Indoor Hard Outdoor Indoor Clay Indoor hard ## 1 1 1 10 ## Indoor Hard Outdoor Clay ## 28 1  Clearly there are registration errors. We can clean this up it by just keeping the court types with more than 30 matches. # remove temp.tab <- temp.tab[temp.tab>30] surf.to.keep <- names(temp.tab) # find idx of Not idx <- !(my.df.matches$Surface %in% surf.to.keep)

# set to NA
my.df.matches$Surface[idx] <- NA  And now we plot the result. # plot percentage of court type over years p <- ggplot(my.df.matches, aes(x = as.numeric(Year), fill = Surface)) p <- p + geom_bar(position = 'fill') print(p) ## Warning: Removed 1 rows containing non-finite values (stat_count).  It seems that hard courts dominated the game over the years. I’m not sure why that is. As a player, I find it easier to play in clay court as you can slide with your legs and that puts lower pressure on the knees. Perhaps hard courts are easier to maintain and that economic argument is extrapolated to tournaments as well. If anyone has a better guess, please leave it at the comments. ## Tennis matches before and after 2000 Tennis is a sport that depends on its equipment. As anyone who plays it can testify, there is a lot of personal choices regarding brands, weight distribution of the racket, strings, grips, brand of tennis balls and so on. A particular type of player will benefit the most from a particular type of racket and string. The physical and mechanical details of tennis makes it that any change in equipment will impact the game. A simple example: if you play more than five games with a set of balls (usually three), they become flat and travel differently in the air. The game becomes slower and that makes it more difficulty to hit winner shots, giving an advantage to a particular strategy of playing. This explains why new tennis balls are constantly introduced within a professional game. One of the biggest systematic changes in tennis happened around 2000. The problem was that the game was losing audience. People were bored of watching the same type of plays and strategies over and over. A tennis match before 2000 was mostly composed of aces, when a player hit a speedy and angular serve, without chance for the adversary to prepare and return the ball or a serve and volley strategy, were the server would hit a slower ball, but would immediately run to the net in order to close the point. If you can find some example of this strategy here. I can personally relate to that boredom. Most of the fun in watching matches comes happens when players are exchanging balls back and forth over the court, one trying to outsmart or overpower the other. The success of both strategies, aces and serve-volley, are related to the speed of the ball. Aces are easier to hit if the ball travels faster. Likewise, a high velocity of the ball does not give chance for the adversary to prepare and counter attack the serve-volley. If the ball from the serve comes slower, the opponent can prepare and hit a passing shot or a lob (high ball) that counters a possible net approach. Back in 2000, the ITF (International Tennis Federation) changed its regulation in order to introduce three new types of balls that would decrease the speed in hard courts and increase it in clay courts. The idea was to make it so that the tennis matches became more interesting for the audience to watch. It is not clear if this change was also set in women tennis, but we will study its effect in the data as well. You can find more details about the change in this article. ### Percentage of aces Now, let’s look at the impact of this change over the percentage of aces from the total points of the game. We will also separate the results by type of surface and gender. Here, we will use the stats files from the repository. The information we need is in ServeBasics. # filenames my.f.m <- 'tennis_MatchChartingProject-master/charting-m-stats-ServeBasics.csv' my.f.w <- 'tennis_MatchChartingProject-master/charting-w-stats-ServeBasics.csv' # set cols my.cols <- cols( match_id = col_character(), row = col_character(), pts = col_integer(), pts_won = col_integer(), aces = col_integer(), unret = col_integer(), forced_err = col_integer(), pts_won_lte_3_shots = col_integer(), wide = col_integer(), body = col_integer(), t = col_integer() ) # build dataframe my.df.serve <- bind_rows(read_csv(my.f.m, col_types = my.cols) %>% mutate(Gender = 'Men'), read_csv(my.f.w, col_types = my.cols) %>% mutate(Gender = 'Women')) # set dates my.df.serve$Date <- as.Date(my.df.serve$match_id,'%Y%m%d') my.df.serve$Year <- format(my.df.serve$Date,'%Y') # get surface idx <- match(my.df.serve$match_id, my.df.matches$match_id) my.df.serve$Surface <- my.df.matches$Surface[idx] # plot it! p <- filter(my.df.serve,str_detect(row,'Total'),!is.na(Surface)) %>% group_by(Year,Gender,Surface) %>% summarise(Percent of Aces = sum(aces)/sum(pts)) %>% ggplot(aes(x=as.numeric(Year),y=Percent of Aces,color = Gender)) + geom_point()+geom_smooth()+ facet_wrap(~Surface) print(p)  The previou result show exactly what we expected. Looking at man’s tennis, the proportion of aces in clay did not show a significant change in 2000. As a matter of fact, the proportion has increased over the years. Clearly there is a break for grass and hard courts in 2000. It seems that players were investing heavily in making aces. This changed dramatically once the new balls were introduced. What I’ve found surprising from the figures is how the difference is not that big. Back in 1995, an average men’s tennis match in a hard court had around 15% of all points from aces. This decreased to only around 8% in 2016. I would be very surprised if someone from an audience even noticed such a change. I was expecting a bigger difference. Perhaps, looking at games from the top 50 players this difference would be more noticeable. As for the woman’s tennis, I don’t acknowledge the same effect. We see from the previous figures that the percentage of aces in the match are in its highest values in the past few years. This result suggests that the balls were not changed in female matches. ### Percentage of Net points Another information to analyze is the percentage of net points. If the change in the balls also affected this part of the game, then we can expect that the proportion of net points has decreased after 2000. As mentioned before, a net approach is easier to counter if the balls travels slower. Let’s have a look in what the data tells us. # files my.f.m <- 'tennis_MatchChartingProject-master/charting-m-stats-NetPoints.csv' my.f.w <- 'tennis_MatchChartingProject-master/charting-w-stats-NetPoints.csv' # set cols my.cols <- cols( match_id = col_character(), player = col_integer(), row = col_character(), net_pts = col_integer(), pts_won = col_integer(), net_winner = col_integer(), induced_forced = col_integer(), net_unforced = col_integer(), passed_at_net = col_integer(), passing_shot_induced_forced = col_integer(), total_shots = col_integer() ) # load data from csv my.df.netpts <- bind_rows(read_csv(my.f.m, col_types = my.cols) %>% mutate(Gender = 'Men'), read_csv(my.f.w, col_types = my.cols) %>% mutate(Gender = 'Women')) # get dates and years my.df.netpts$Date <- as.Date(my.df.netpts$match_id,'%Y%m%d') my.df.netpts$Year <- format(my.df.netpts$Date,'%Y') # get surface idx <- match(my.df.netpts$match_id, my.df.matches$match_id) my.df.netpts$Surface <- my.df.matches\$Surface[idx]

# plot it!
p <- filter(my.df.netpts,!is.na(Surface)) %>%
group_by(Year, Gender, Surface) %>%
summarise(Percentage of net points = sum(net_pts)/sum(total_shots)) %>%
ggplot(aes(x=as.numeric(Year),y=Percentage of net points, color = Gender)) +
geom_point()+geom_smooth() + facet_wrap(~Surface)

print(p)


As expected, we find similar information from the previous analysis. Not much of a change in seen in clay courts after 2000. This is the opposite of hard courts, where the percentage of net point decreased dramatically after 2000. In this case, however, the different is much larger. The percentage of net points back in 2000 was around 35%. That decreased to approximately 20% in 2016. A steeper difference from the change in the proportion of aces seen before.

As for women tennis, we again don’t see a big change. This result also suggests that the new balls were not implemented in woman’s tennis.

## Conclusion

In this post I showed some simple graphical analysis of the evolution of tennis over time. I find that the change in the tennis ball’s composition back in 2000 made a significant impact in the game, specially in grass and hard courts. The proportion of aces and net points changed significantly.

In the next post, scheduled for next week, I will make a data analysis of tennis players. I will focus first in the big names such as Federer and Djokovic and later will analyze the Brazilian scene and the records of the greatest Brazilian player (so far, i hope 🙂 ), Gustavo Kuerten - Guga. As you can see, I am a big fan of him.

I hope you enjoyed this post. Fell free to leave your message at the comments.