And I appreciate you getting this far despite your reservations. Until we drill down to much more specific situations—and maybe even then—these tour averages are no more than curiosities. These averages are ultimately only tools to better quantify the accomplishments of specific players. With the recent buzz about Elo rankings in tennis, both at FiveThirtyEight and here at Tennis Abstract , comes the ability to forecast the results of tennis matches.
For inferring forecasts from the ATP ranking, we use a specific formula 1 and for Pinnacle, which is one of the biggest tennis bookmakers, we calculate the implied probabilities based on the provided odds minus the overround 2. What we see here is how many percent of the predictions were actually right. Interestingly, the Elo model of Riles is outperformed by the predictions inferred from the ATP ranking. Since there are several parameters that can be used to tweak an Elo model, Riles may still have some room left for improvement.
However, just looking at the percentage of correctly called matches does not tell the whole story. Resolution measures how much the forecasts differ from the overall average. In other words, the more extreme and still correct forecasts are, the better. As we can see, the predictions are not always perfectly in line with what the corresponding bin would suggest. However, there are still two interesting cases marked in bold where sample size is better and which raised my interest. For the Riles model, this would maybe be a starting point to tweak the model. The Brier score combines Calibration and Resolution and the uncertainty of the outcomes into a single score for measuring the accuracy of predictions.
The models of FiveThirtyEight and Pinnacle for the used subset of data essentially perform equally good. Then there is a slight gap until the model of Tennis Abstract and the ATP ranking model come in third and fourth, respectively. The Riles model performs worst in terms of both Calibration and Resolution, hence, ranking fifth in this analysis. To conclude, I would like to show a common visual representation that is used to graphically display a set of predictions.
The closer one of the colored lines is to the black line, the more reliable the forecasts are. Given that we only investigated one tournament and therefore had to work with a low sample size predictions , the big swings in the graph are somewhat expected.
The truth about unforced errors | Tennismash
Still, we can see that the model based on ATP rankings does a really good job in preventing overestimations even though it is known to be outperformed by Elo in terms of prediction accuracy. To sum up, this analysis shows how different predictive models for tennis can be compared among each other in a meaningful way. This is something I will spare for later. Peter Wetz is a computer scientist interested in racket sports and data analytics based in Vienna, Austria.
The betting market in itself is not really a model, that is, the goal of the bookmakers is simply to balance their book.
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This means that the odds, more or less, reflect the wisdom of the crowd , making it a very good predictor. As an example, one instance, where Pinnacle was underconfident and all other models were more confident is the R32 encounter between Ivo Karlovic and Jared Donaldson. One factor at play here might be that these were the US Open where more US citizens are likely to be confident about the US player Jared Donaldson and hence place a bet on him. As a consequence, to balance the book, Pinnacle will lower the odds on Donaldson, which results in higher odds and a lower implied probability for Karlovic.
When is an error unforced? Many errors are obviously forced or unforced, but plenty fall into an ambiguous middle ground. Most of the unforced error counts we see these days—via broadcasts or in post-match recaps—are counted by hand. A scorer is given some guidance, and he or she tallies each kind of error. Of course, any attempt to sort missed shots into only two buckets is a gross oversimplification.
In other words: Did you make it, did you miss it, or did you fail to even get a racket on it? Errors are no different.
In this case, Kasatkina was able to get a racket on the ball, but missed the shot, resulting in what most scorers would agree was a forced error:. We might discover that Kasatkina lets 35 of go for winners, or that Simona Halep lets only 15 go for winners and gets 70 back in play, or that Anastasia Pavlyuchenkova hits an error on 30 of the attempts. My point is this: With more granular data, we can put errors in a real-life context.
Over the course of down-the-line forehands, those factors would probably even out. While a typical best-of-three match will give us a few hundred shots to work with, this level of analysis can only tell us so much about specific shots. We would be able to quantify whether, for instance, Andy Murray gets more of the most unreturnable shots back in play than Novak Djokovic does. This sort of analysis would be engaging even for those fans who never look at aggregate stats.
Fortunately, the technology to accomplish this is already here. Researchers with access to subsets of Hawkeye data have begun drilling down to the factors that influence things like shot selection. Someday, tennis fans will look back on the early 21st century and marvel at just how little we knew about the sport back then. Today, I want to show you something else I rolled out over the offseason: sequential point-by-point stats for more than , matches.
Traditional match stats can do no more than summarize the action. 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.
The centerpiece of each page is a win probability graph, which shows the odds that one player would win the match after each point.
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These graphs do not take player skill into account, though they are adjusted for gender and surface. As I watched that semifinal again, I paid close attention to the unforced errors, and the official tally was indeed off … by just two. And had 42 winners. In the same vein, you can see just how good Sorana Cirstea was in that second set today: the official tally had her committing only one unforced error, while I had her committing only two.
Regardless, the main lesson here is that when you see those unforced error counts on your broadcasts, or when you use TennisTV, you can be reasonably confident that they are a fairly close reflection of what is happening on a tennis court. Looking at the numbers might lead you to identify certain trends, as well as to ask many questions. For example, how often does a player win a point BP down or down after getting their first serve into play? Seems to me that that counts for more than getting a cheap point when or up.
Does a play commit UFEs attempting to keep a neutral ball rally going, or while attempting to force the play?https://temunsochalmau.cf
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How much risk is appropriate eg for a player ranked 50 playing one of the top 5? You make a lot of good points and suggestions for other stuff to track , Andrew. Particularly if you add video to the mix and go back and check out a particularly problematic game. Yes, agree with comments above. UFE is only one way of explaining what happens during a match and although interesting and potentially valuable, most of us watching a match have a pretty good idea of whats going on too without seeing the stats.
They tend to confirm for me what I have been seeing rather than illuminate something new. What you would like, either as a coach or a fan, is to know when those UFEs happened, and then have a chance to go back and watch any problematic patches. You might find stuff there that you had no idea about. I fully expected Ferrer to hit way more FHs than BHs, and instead, I found that he hit just 10 more in the entire match.
I also found that he tends to hit more BHs than FHs during return games. There are all sorts of things that Sumyk could see in statistics…provided they are interesting statistics, and not just raw totals at the end of the match.
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UFE is generally a useless stat. Almost every error is somewhat forced; almost every error is somewhat unforced. We disagree, Ilija.
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I do think UFEs are revealing, but not so much as a raw total at the end of the match. About the stat you mentioned… I wrote a post about it today! I do think that it would be great if we could have Forehand and Backhand Efficiency Ratings for every match, for every player. That would be really awesome.