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Tour Finals Semifinals Scenarios
Here are the scenarios that match the probabilities posts. I may have missed one in Group Hewitt, but if so, I donât want to know at this point. Brain is cooked.
Group Kuerten
Djokovic wins, Zverev wins: Djokovic (1), Zverev (2)
Djokovic wins, Isner wins in straight sets: Djokovic (1), Isner (2)
Djokovic wins, Isner wins in three sets: Djokovic (1), Zverev (2)
Cilic wins, Zverev loses: Cilic (1); Djokovic (2)
Cilic wins, Zverev wins: Djokovic (1), Zverev (2)
Group Hewitt
Anderson wins, Nishikori wins: Anderson(1), Nishikori (2)
Anderson wins in straight sets, Thiem wins in three sets: Anderson (1), Nishikori (2)
Anderson wins in straight sets, Thiem wins in straight sets without losing a game: Anderson (1), Thiem (2)
Anderson wins in straight sets and loses 8-10 games, Thiem wins in straight sets losing <2 games: Â Anderson (1), Thiem (2)
Anderson wins in straight sets and loses <8 games, Thiem wins in straight sets losing <3 games: Â Anderson (1), Thiem (2)
Anderson wins in straight sets and loses <7 games, Thiem wins in straight sets losing <4 games: Â Anderson (1), Thiem (2)
Anderson wins in straight sets and loses <6 games, Thiem wins in straight sets losing <5 games: Â Anderson (1), Thiem (2)
Anderson wins in straight sets and loses <5 games, Thiem wins in straight sets losing <7 games: Â Anderson (1), Thiem (2)
Anderson wins in straight sets and loses <4 games, Thiem wins in straight sets losing <8 games: Â Anderson (1), Thiem (2)
Anderson wins in straight sets and loses <3 games, Thiem wins in straight sets losing <9 games: Â Anderson (1), Thiem (2)
Anderson wins in straight sets and loses <2 games, Thiem wins in straight sets: Â Anderson (1), Thiem (2)
Anderson wins in straight sets and loses 2-3 games, Thiem wins in straight sets and loses 10-12 games: Â Anderson (1), Thiem (2)
Anderson wins in straight sets, Thiem wins in straight sets in all other scenarios not listed above: Anderson(1), Federer(2)
Anderson wins in three sets, Thiem wins in straight sets: Anderson (1), Federer (2)
Anderson wins in three sets, Thiem wins in three sets: Anderson (1), Nishikori (2)
Federer wins in straight sets but loses 6 or more games, Nishikori wins in straight sets: Anderson (1), Nishikori (2)
Federer wins in straight sets but loses <6 games, Nishikori wins in straight sets: Federer (1), Anderson (2)
Federer wins in straight sets, Nishikori wins in three sets: Federer (1), Anderson (2)
Federer wins in three sets, Nishikori wins: Anderson (1), Nishikori (2)
Federer wins in straight sets, Thiem wins: Federer (1), Anderson (2)
Federer wins in three sets, Thiem wins: Anderson (1), Federer (2)
Federer wins in straight sets without losing a game, Nishikori wins in straight sets and loses < 2 games: Federer (1), Nishikori (2)
Federer wins in straight sets and loses 1 game, and Nishikori wins in straight sets without losing a game:Â Federer (1), Nishikori (2)
Tour Finals Group Hewitt - The Gory Probabilities...and I Mean Gory!
I determined there are 23 (see update) semifinal scenarios for Group Hewitt, with four different combinations of players that can make the semis. Yeah, 21. I canât promise I didnât miss the odd combination where a player won seven games in a set, but I think I got all or most of them. Â
More than half of the scenarios involve both Anderson and Thiem winning in straight sets, putting Anderson in the top spot with everyone else tied at 1-2 in matches and 2-4 in sets. In that scenario, the number of games Federer, Thiem and Nishikori win will matter.  In the four posts Iâve done like this, Group Hewitt is the first where the game level probabilities matter. It essentially translates into how likely it is that a player will win a set by a certain score.  Â
Here are the Group Hewitt probabilities of winning the match, a game and a set, and then the probabilities of 2- and 3-set wins.
The next chart shows the probability of the first 21 scenarios occurring, and if it occurs, who the #1 and #2 players are expected to be from Group Hewitt. UPDATE: There are 2 scenarios (new to me) in which Anderson does not qualify for the semifinals, unlike my original post, but they are extremely remote...basically a 0 percent chance that does not change the probabilities in a significant way, so I have not updated them below. You also will notice that all the Anderson-Thiem possibilities show as 0.0%, but they are actually slightly positive...just way to the right of the decimal. In the later charts you will see they donât add up to much.
You can add those up to see a playerâs chances of making the semis, and the chances that one of the three combinations occurs. Â Here they are, added for you:
Thereâs a little rounding error in there, which accounts for the 99.9%. Itâs probably buried in one of the Anderson-Thiem probabilities, and I donât have the stomach to dig back in there and find it.
Put the Group Hewitt info together with the info from Group Kuerten, and you get 14 possible combinations for the semifinals. Â The most likely semifinals are Djokovic (Kuerten #1) and Nishikori (Hewitt #2) on one side, and Anderson (Hewitt #1) and Zverev (Kuerten #2) on the other.
Tour Finals Group Kuerten - The Gory Probabilities
Unlike the Milan NextGen finals, I did not see the ATP release the semifinal scenarios for the two groups in the Tour Finals. Â Accordingly, I tried it myself.
Group Kuerten is the easy one, as I found only 5 scenarios for 3 maximum combinations. I used the same Markov technique that I used in the NextGen posts, except that I used an average service point probability for the matches that was closer to the playersâ actual probabilities, rather than the Tour average. It makes very little difference.
From service point probabilities, you can calculate the probability that a player will win a game, and when you know that, the probability of winning a set.  With that info, you can figure the probabilities that a player wins the final round robin match in straight sets or three sets. And unlike Milan, the game probabilities matter, because in Group Hewitt thereâs a good chance the games won percentage tiebreak will come into play. They donât matter for Group Kuerten, but I have included them in the table anyway.
Here are the probabilities for Group Kuerten as I have calculated them:
Now with these base figures, you can estimate the probability of each of the 5 scenarios I determined. Â This chart shows the probability of each scenario occurring, and if it occurs, who the #1 and #2 players are expected to be from Group A. Â Youâll notice that Djokovic has already qualified for the semifinals, but there is still a chance he can be the #2, which would require both Cilic and Isner to win (putting Djokovic and Cilic both at 2-1, with Cilic winning the head-to-head).
You can basically add those up to see a playerâs chances of making the semis, and the chances that one of the three combinations occurs. Â Here they are, added for you:
Interesting that Cilic can be #1, but not #2. The opposite is true for Isner.
Next up, Group Hewitt, which was a nightmare...
Milan Singles Group B - The Gory Probabilities
The ATP has 9 semifinal scenarios for Group B. As explained in the post about Group A, these scenarios are all the ways in which the outcomes of tomorrowâs matches in Group B can produce an outcome that matters. In Group B, there are three different combinations of players that can make the semis.
Here are the Group B probabilities of winning the match and a set, and then the probabilities of 3-, 4- and 5-set wins
This chart shows the probability of each scenario occurring, and if it occurs, who the #1 and #2 players are expected to be from Group B. Â Youâll notice that de Minaur has already qualified for the semifinals, but there is still a chance he can be the #2.
You can add those up to see a playerâs chances of making the semis, and the chances that one of the three combinations occurs. Â Here they are, added for you:
Put that together with the information from Group A, and you get 14 possible combinations for the semifinals, eight of which are on the Group B side, and six from the Group A side. The most likely semifinals are Tsitsipas (A #1) and Rublev (B#2) on one side, and de Minaur (B #1) and Tiafoe (A #2) on the other.

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Milan Singles Group A - The Gory Probabilities
The ATP has 11 semifinal scenarios for Group A. To be clear, these scenarios are all the ways in which the outcomes of tomorrowâs matches in Group A can produce an outcome that matters. Of course there are not 11 different combinations of players that can make the semis from Group A...there are actually only four, but there are 11 unique ways to get there.
I have not reproduced the text of those scenarios here, but I expect the ATP will publish them on its web site soon.
There are 11 scenarios for four maximum combinations because many depend on whether a player wins in 3, 4 or 5 sets. Accordingly, we need to determine each playerâs probability of winning a set.
We can back into that by first projecting the match probability for each player. Iâve done that using a mix of my Match Stats-based forecast (90%) and my hard court-specific ELO forecast (10%).  From there, using a Markov model, you can mathematically back into each players probability of winning a service point. From service point probabilities, you can calculate the probability that a player will win a game (no ad), and when you know that, the probability of winning a (short) set. With that info, you can figure the probabilities that a player wins the final round robin match in straight sets, four sets or five sets. Â
Here are those probabilities as I have calculated them:
Now with these base figures, you can estimate the probability of each of the ATPâs 11 scenarios. This chart shows the probability of each scenario occurring, and if it occurs, who the #1 and #2 players are expected to be from Group A. Youâll notice that Tsitsipas has already qualified for the semifinals, but there is still a chance he can be the #2.
You can basically add those up to see a playerâs chances of making the semis, and the chances that one of the four combinations occurs. Here they are, added for you:
Next up, Group B...
Are These ATP Players Similar?
While researching something else that required extracting cumulative match stats for ATP players, I got sidetracked with wondering whether any two players are alike, or whether everyone has a unique set of skills, or at least unique ability in certain skills areas. Â There probably are lots of ways to measure this, but I didn't start out with this goal, so I wanted to use the data I had already extracted.
I used the cumulative match stats of the Top 200 players (as of 10-7-2018), as they performed on hard courts over the last two and a half years against opponents ranked <=300. Â I eliminated 11 players with fewer than 150 service games from the data set, since that's a fairly poor sample relative to the time period covered.
I placed each player in a "bin" with respect to each of these seven stats: Â Ace%, DF%, 1stIn%, 1stWon%, 2dWon%, 1stRetWon% and 2dRetWon%. Â I labeled them with letters, depending on the bin I put them in. Â
Players greater than 1.5 standard deviations from the mean in a stat category got Tier A,Â
Players between 1.5 standard deviations and 0.5 standard deviations got Tier B,Â
Players between 0.5 standard deviations and -0.5 standard deviations got Tier C,Â
Players between -0.5 standard deviations to -1.5 standard deviations got Tier D, andÂ
Players more than -1.5 standard deviations from the mean got Tier F.*Â Â
In all categories but DF%, the highest numbers got A's and the lowest numbers got F's.
*Many of those stat categories are not normally distributed, so we don't have a nice bell shape. Â In particular, Ace%, DF%, 1stIn% and 2dRetWon% are pretty warped. Â Ace% is particularly whacked out, as there are a number of extraordinary ace artists, but only Yoshihito Nishioka is leaps and bounds worse than the mean.
In a very simplistic method, I simply strung the tier letters together to see if that would indicate similarities between the players in terms of style or performance. For example, John Isner is an A in all the serve categories, except a B on double faults, and an F in the two return categories. Â Following the stat order above, he is coded as a ABAAAFF. Â Are there any other players in the Top 200 who are ABAAAFF's on hard courts? Â Nope.
In fact, out of the 189 players, only 10 have the same code as another player and no three players have the same code. Â Here is a list of the players with the same code as another player, and their tier code if you are interested in matching the tier to the stat:
Taylor Fritz and Aljaz Bedene (BBDBCCC)
Frances Tiafoe and Leonardo Mayer (CCCCBCC)
Jaume Munar and Daniil Medvedev (CCCCCBB)
Nicolas Mahut and Pablo Cuevas (CCCCCCD)
John Millman and Kei Nishikori (DBCCBBB)
Philipp Kohlschreiber and Miomir Kecmanovic (DBCCBCC)
Yannick Maden and Gilles Simon (DBCDCBA)
Jason Jung and Tatsuma Ito (DCBDDBC)
Thomas Fabbiano and Radu Albot (DCBFCBB)
Diego Schwartzman and Kamil Majchrzak (DCCDCBA)
Eh, are those some weird pairings or what? Â If I included height or age in the tiers, most of these guys would be unpaired immediately. Â I don't think Millman and Nishikori are terrible comps in terms of style, although they clearly do not have the same talent (or at least performance). Â The Munar and Medvedev pairing is ridiculous.
Part of the reason for the weird pairings is that there are only five bins.  You can be in the same bin with someone in a category, but be significantly better or worse than your mate in that category. The margins on the tour are small.  For instance Bedene and Fritz are tightly paired in every category but one:  1stRetWon%, where Bedene's mark is 1.3% higher (but still in the same tier).  Might not sound like much, but that's a huge difference in terms of results.  Yet it isn't that big a difference in terms of style, although they are in vastly different stages of their career arcs.
The more important reason for weird pairings is the difference in competition that these players face.  In other words, the match stats are a function not just of the player's abilities but his opponents' abilities. Taking Bedene and Fritz again, while they are very alike in every category (usually very much alike), the average rank of Bedene's hard court opponents the last two and a half years is about 88, whereas Fritz's number is 114.  Rankings are not the end-all, be-all of opponent quality, but that's a big difference.  Except for Jung/Ito, the differences in opponent ranking for each of these pairings is huge.  The difference between average opponent rank is largest in the Schwartzman/Majchrzak and Kohlschreiber/Kecmanovic pairings.
It would be nice if we could normalize the stats to see what they would be against a common opponent, and then re-bin them and do the same exercise as above. Â There are probably lots of ways to do this -- and none of them are particularly easy -- but here's what I did for purposes of this post. Â Typically when you want to normalize sports statistics, you adjust for the context in which they play (in baseball for example, the ballparks they play in and the leagues they play in, particularly when comparing across eras). Â But context is quite difficult in tennis. Â We can start by limiting to a particular surface, but the wider "league" context is problematic. Â Nominally, tennis players play against the universe of professional players, but the reality is that every tennis player faces a unique mix of opponents during any particular window. Â Using a dataset that includes the Top 200's matches against only the Top 300 helps a bit, but in two and a half years on a hard court, each Top 200 player with a significant number of matches on this surface plays only about 50 unique opponents in the Top 300, often many fewer. Â In other words, every tennis player is playing in a different league with its own quality.
So my approach to normalization (at least for now), is to determine a players' opponents during the two and a half years on hard courts, determine their collective (weighted) means in each stat category against the Top 300, and compare that to the overall mean for the Top 200 in those categories. Â This should tell us how strong their own universe of opponents is, relative to the Top 200 average in each category. Â We then use that universe's positive or negative deltas from the overall mean to adjust the subject player's stats in that category.Â
So, for instance, if Taylor Fritz's universe of opponents is better than average on 1stRetWon%, we can boost Taylor Fritz's 1stWon% (the serve side) to simulate what he would achieve if he faced a merely average opponent. Â I ignored for this purpose DF% and 1stIn%. Â Both of those should be influenced by opponents' return ability, but unlike the other five categories, there isn't a directly opposing stat to make it relatively easy. Â Ace% has OppAceAgainst%, 1stWon% has Opp1stRetWon%, 2dWon% has Opp2dRetWon%, 1stRetWon% has Opp1stWon% and 2dRetWon% has Opp2dWon%. Â I don't know quantitatively how good returners affect DF% and 1stIn%.
After re-binning using the normalized stats, I got 11 groups of similar players, including one instance of three players being similar to one another.
Miomir Kecmanovic and Evgeny Donskoy (CBCCBDC)
Tim Smyczek and Bjorn Fratangelo (CBCDCCC)
Taylor Fritz, Lukas Lacko and Andreas Seppi (CBDBCCC)
Denis Kudla and Jaume Munar (CCCCCCB)
Frances Tiafoe and Leonardo Mayer (CCCCCCC)
Lukas Rosol and Gregoire Barrere (CCCCDDC)
Dennis Novak and Michael Mmoh (CDBDDCC)
Pablo Carreno Busta and Guido Pella (DBBDBCB)
Yannick Maden and Adrian Mannarino (DBCDCBB)
Damir Dzumhur and Radu Albot (DCBFCBB)
Nikoloz Basilashvili and Dominik Koepfer (DDCDCCC)
Admittedly I don't know all these names, e.g., whether Barrere is like persona non grata Rosol, but generally, this list based on the normalized stats looks a lot more reasonable than the first one. In fact, there are a couple of really good comps here, including #2, #3, #4 and #7. Nothing seems patently ridiculous to me, although Basilashvili may be in the process of breaking up the marriage in #11.
You will note that only one pairing from the unnormalized version survived normalization:  Frances Tiafoe and Leonardo Mayer. It still seems strange to think of them as paired in this way, but actually their normalized stats are closer together than their unnormalized stats, so the pairing isn't just because they rate in the middle tier in every single category.
I've often wanted to do Bill James-style similarity scores for tennis players, but have found the task daunting.  James' sim scores for baseball players was pretty straightforward, because he did not use normalized stats. Using the same method for tennis would require only an adjustment to the points associated with various stat differences between the players, but I donât think we can get away with that in tennis. I once created a spreadsheet (many years ago) that used baseball players' stats normalized for era and ballpark, and then applied sim scores.  That sounds appealing for tennis, but normalizing tennis is not quite as simple, for the reasons stated above:  every tennis player is essentially in his own league.  And the method I used here, which was pretty tedious despite its simplicity, was only for two and a half years, not a career span. Â
Anyway, it's something to dream about.
Tennis Stuff I Didnât Know (about) Juan Monaco and Julien Benneteau
1. Juan Monaco played in the Finals at Dusseldorf three times (winning twice). On each occasion, he turned around and played Roland Garros the next week, and lost in the first round all three times. Â
Monaco played 22 finals in his career, and he played the immediately following week 14 of those times. He was 8-6 in those 14 turnaround matches. Take out the weeks after his Dusseldorf finals and he was 8-3 in turnarounds.
2. Julien Benneteau played in the Finals at Kuala Lumpur three times (winning none). On each occasion, he turned around and played Beijing the next week, and lost in the first round all three times.
Benneteau played 10 finals in his career, and he played the immediately following week 9 of those times. He was 3-6 in those 9 turnaround matches. Take out the weeks after his Kuala Lumpur finals and he was 3-3 in turnarounds.
Popcorn Scores: Beijing and Tokyo (R32)
Here are the Popcorn Scores for the first round matches at Beijing and at Tokyo, with red being the hottest, butteriest popcorn, grey being ordinary popcorn and blue being cold, stiff popcorn with very little salt.
Popcorn Scores: Chengdu and Shenzhen QFs
Here are the Popcorn Scores for the quarterfinals matches at Chengdu and at Shenzhen, with red being the hottest, butteriest popcorn, grey being ordinary popcorn and blue being cold, stiff popcorn with very little salt.
Gonna be some runninâ in that second match, and I bet FAA is going to see something he has never seen before in his match against Tomic.

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Popcorn Scores: Chengdu and Shenzhen (R16)
Here are the Popcorn Scores for the round of 16 matches at Chengdu and at Shenzhen, with red being the hottest, butteriest popcorn, grey being ordinary popcorn and blue being cold, stiff popcorn with very little salt.
Um, pretty easy to spot the winner, since thereâs a finals-level match in round 2.
Stealing Ideas from Golf:Â The Hall of Fame
Tennis has the International Tennis Hall of Fame in Newport, Rhode Island. Newport is a very attractive New England town. It is fairly traditional Hall of Fame, with memorabilia and history exhibits. During the week of the induction ceremony, they hold a menâs 250 event there, on grass. The building is ivy covered. It is a relatively small facility, but it has a lot of charm. Â
You get to the tennis Hall of Fame by driving down Bellevue Avenue, the main thoroughfare through town, and it is one of the buildings you pass, among the other buildings. I have only been to Newport one time, and the first time I drove down Bellevue, I didnât even see the Hall of Fame (I was on my way somewhere else). It was only on the way back that I noticed it. At the next intersection, there is a Social Security Administration building. Dunkinâ Donuts and CVS are only a block away. You stay wherever you would stay if you were not going to the tennis Hall of Fame.
Golf has the World Golf Hall of Fame in St. Augustine, Florida, not far from its headquarters in Ponte Vedra, Florida (where the ATP also has its headquarters). Golfâs Hall of Fame is part of the bigger World Golf Village, which is like a mini-Disney World for Golf. In addition to the more traditional Hall of Fame setup, with plaques and memorabilia, it has shopping, hotels, an IMAX Theater that shows films that have nothing to do with golf, and importantly, two golf courses. It has a giant lake near the main buildings, and on the sidewalks surrounding this lake they have embedded plaques with names of Hall of Famers. You can buy an affordable brick with a message of your choice that will be installed on the sidewalk near the champion of your choice.
You get there by driving down I-95, exiting via the signs that are dedicated solely to the World Golf Village (itâs actually called World Golf Village Boulevard), and then driving a little ways to access the various features. The buildings are nothing to get excited about -- pink Florida facades, mostly, and faux Spanish architecture -- not nearly as charming as the Newport building. Actually, not charming at all. Thereâs nothing else close by, except for cookie-cutter Florida subdivisions (albeit in nice surroundings).Â
What can the tennis Hall of Fame learn from golfâs Hall of Fame?
Let me be clear: I like the charm of Newport. World Golf Village is not charming.  But the creators of World Golf Village decided to sacrifice charm for some out-of-the-Hall-of-Fame-box thinking. World Golf Village isnât just a Hall of Fame. Itâs a destination.
The International Tennis Hall of Fame is not a destination. Â Itâs something you might do if you are in Newport for another reason, or nearby in New England. People actually plan trips to the World Golf Village and stay for a week. Â They donât just visit if they happen to already be in St. Augustine.
Why is that the case? It isnât purely location. St. Augustine is closest, but thatâs a tourist town, not a real city. World Golf Village is 30-45 minutes south of Jacksonville, and a good hour and a half north of Orlando. It was built in the middle of nowhere, because it wasnât trying to draw solely from nearby populations. It was designed to be the focus of your trip.
Accordingly, it has all the required elements of a destination. It has places to eat on-site (e.g., the Caddyshack restaurant, with Bill Murray on board). It has places to stay on-site, filled with pictures of golfers, and golf courses. Accommodations are, not surprisingly, golf-themed. And, there are things you can do besides touring the museum...namely, you can play a lot of golf on two World Golf Village courses. These courses are named after Hall of Famers. They were designed with the help of Hall of Famers. Â
The International Tennis Hall of Fame does not have these features, or to the extent it does, they are in no way packaged together. You can eat in the general area, but you could do that anyway. You can stay in a hotel, but itâs just a hotel in Newport you would stay at anyway. There is a grass court that hosts the aforementioned ATP event, and you can play on it for $120/hour, but there arenât many courts and you arenât allowed to stay on the court all day, even if you can afford it. And really, given where the tennis Hall of Fame is located, why would you even have your racquet handy?Â
Iâm the last person who would want to Disneyfy something, but I think the International Tennis Hall of Fame needs to move in the direction of the World Golf Village...at least a little bit. Unfortunately, that may mean leaving Newport.
There is a good reason the Hall of Fame is in Newport. The first US National singles championship was played there in the 1800s. In that regard, itâs similar to the Pro Football Hall of Fame in Canton, Ohio (where the NFL was founded in 1920) and the very successful Baseball Hall of Fame in Cooperstown, New York (where the first baseball game was said to have been played by Abner Doubleday, though that certainly is not true). Canton is not charming, but Cooperstown has charm in spades. The entire image of the town is built around the Baseball Hall of Fame.
So tennisâs Hall of Fame fits that profile well. But tennis is more like golf than football or baseball. No one attending the football or baseball halls of fame would think theyâd get 22 or 18 people together to play a game (unless you are the parent of a baseball Little Leaguer). By contrast, golf fans at the World Golf Hall of Fame sure as hell want to play some golf. And if I go to a tennis hall of fame, Iâd like to play some tennis on some courts the pros play on, especially if they are grass courts. But for that to happen, the tennis Hall of Fame has to be a real part of my plans, not something I see on the way to something else.
Tennis outgrew Newport pretty quickly in the last century.  By 1914, they had to move the singles championship to New York (itâs now the US Open). The Hall of Fame stayed behind to eventually host an ATP 250. It isnât clear to me why it doesnât host a womenâs event, since women are members of the Hall of Fame. Iâm sure there are many âreasonsâ (meaning money), but its ridiculous. In fact, I would propose a joint menâs and womenâs event during the induction week, but the fact is, they donât have room for that at the Newport facility.
There must be another venue, somewhere in the world, that has more charm than the World Golf Village, but could be its own tennis complex, with on-site restaurants, on-site accommodations and lots and lots of tennis courts. There was a potential opportunity years ago in Ponte Vedra Beach, Florida, where the ATP headquarters is. When the ATP headquarters originally was built in the early 90s, it had lots of courts on all the various surfaces. Sawgrass, where The Players Championship is played and the PGA is headquartered, is literally down the street. The surroundings are beautiful and sufficiently distant from Jacksonville to make it a getaway. Less than a mile away is the Atlantic Ocean. That facility could have gone a long way towards an International Tennis Hall of Fame destination site, without going full Disney.
Unfortunately, those facilities were underutilized, in part because of the USTA training facility in Orlando. The facilities since have been sold (or maybe leased) to a local private school, which has paved all the surfaces for the high school tennis team. The ATPâs headquarters is now in a nice tree-shrouded office building across the street.
I will admit that it may be hard to find a facility as charming as Newportâs that also has these other features. I suspect thatâs nearly impossible in the U.S., particularly if you take into account a site that has some historical significance for tennis. However, tennis is a truly international sport, so its Hall of Fame doesnât have to be in the US with all the other halls of fame. Â
Golf focused on making its hall of fame a destination. It did not require the site to have a historical connection, and essentially built it from the ground up. Thatâs a bit ambitious for tennis, but I would suggest that tennis loosen the historical restriction, as fans are more interested in what they can do while visiting a hall of fame than they are the historical basis for the hall of fameâs location. And rather than market it as âWhile you are in the area...â, how about âBring your racquet...â?
Popcorn Scores: Chengdu and Shenzhen
Hope you like late night popcorn for these matches in China. If ever you needed Popcorn Scores to figure out who to watch, this is the week in Chengu and Shenzhen.
Catch Monfils on his personal Asian swing, since he got a head start last week at the Kaohsiung Challenger (and won it).
2018 US Open LOS and COM Awards
In a previous post, I described the LOS and COM award concepts.  Hereâs the full list of LOS awards and COM awards, respectively, from this yearâs US Open:
US Open LOS âAwardsâ
I would suggest not interpreting this as âthe losing player should have won,â as that denies too much credit to the winning player. Â I think it is more along the lines of âthey are probably kicking themselves.â
I didnât watch all of these, though I did watch many of them. Â Some of these on the list are exactly what I was trying to capture (e.g., Vekic, Ostapenko, Federer, Vondrousova losing to Tsurenko). Â To my mind, only one of these is seriously questionable (Basilashvili v Nadal), but thatâs probably because Iâm thinking Basilashvili had no chance anyway. Â
You might be surprised by Duckworth getting a LOS in the Murray match, but Murray did not play particularly well in that match and did not control the action, in my opinion. Note that Berankis l. Chung is asterisked because Berankis retired.  Before his injury in the third set, he was toe-to-toe with Chung, so he probably shouldnât get a LOS, but the formula doesnât know that.
It cannot escape attention that three of the 9 legit LOS awards on the menâs side go to John Millmanâs opponents.
The number of men who get a LOS is about in line with what I think it should be (10% or so), but the number of LOS awards for women is a tad high, I think. Â I considered revising the 10% LOS buffer to 15% all-around, or just on the womenâs side. Â Youâd lose five on the womenâs side at 15% (Wang, Ostapenko, Safarova, Kuzmova and Vekic), but two of those were matches I would have targeted for a LOS, so I donât want to lose them. Â At 12% Iâd get Vekic back. Â On the menâs side I would lose Isner and Duckworth at 15%, which would be fine. Â Iâm sticking with a 10% LOS buffer across-the-board for now. Â
US Open COM Awards
A much smaller list, probably because I required 1.5 standard deviations of difference instead of 1 SD, which I used in the LOS. Â I think this list is actually pretty good. Â Most would have been indicated by lopsided scores anyway, but not Raonic d. Berlocq and Kyrgios d. Albot, both of which went four sets.
I expected to see Osaka d. Sasnovich on here, since it was the blowout match of the tournament. Â But because Sasnovich was credited with 28 UEs in only 12 games (55% of the points Osaka won), Osakaâs dominance was significantly enhanced by Sasnovichâs mistakes.
***
As for the match that started all this - Coric v. Tiafoe - because we know Coric wins, he wonât be getting a LOS. And, because Tiafoe is clearly not in control of this match, even though he is up 2 sets to 1 when I paused the video, he wonât get a LOS either. It remains to be seen whether Coric will get a COM. After three sets, heâs just below the 13% cutoff, even though he is losing. Iâll update when I make it through the last two sets. UPDATE: Coric gets a COM, easily.
Command of Match (COM) and Lost Opportunity Score (LOS)
Iâve been watching the Davis Cup tie between Borna Coric and Frances Tiafoe. Â
The first set, won by Tiafoe in a tiebreak, is largely marked by Borna Coric showing an incredible lack of touch around the net (he cannot volley at all*) and the near impossibility of keeping his forehand in the court. Â
*Separate research project: Check The Match Charting Project to see if Coric is the worst volleyer in the Top 50. Starting hypothesis is that he is.
Then when the second set kicks off, Frances Tiafoe canât win a game, and barely any points. His energy level plummets and his serve is about as bad as you will see from a male professional tennis player. He loses the second set 1-6, and proceeds to carry that over into the third set. In an 11 game stretch, he wins only 1 game, and only 14 of 53 points, resulting in a 0-4 deficit in the third set. Although his aggressiveness waned, I think the most important factor is that Coric stopped making those horrendous unforced errors, at which point Tiafoeâs weaknesses were all brought to the fore (or is it âfoeâ?).
But at 4-0, Coric started making horrendous errors again. I mean horrendous. At one point, he makes 7 in a row, and voila, Tiafoeâs energy returns. Tiafoe does not play great in the rest of the third set, but Coric is so bad that Tiafoe comes back and wins the second set tiebreak. (In fairness, the tiebreak itself is fairly well-played by both players).
As Iâm writing this, I have not started the fourth set. I know Coric wins the next two sets because I know Croatia is in the finals, but I actually havenât looked at the set scores for the final two sets. Based on what Iâve seen, it seems almost certain that if Coric can keep his forehand in the court, he will win, and win easily. For all the Twitter talk of how Tiafoe was a warrior in the match (apparently forgetting that 11 game stretch), he shows no sign he can control the points. His backhand is merely steady, and his wack-a-doo forehand stroke just rolls the ball around the court. In other words, this match is not on his racquet.
I paused in my viewing of the match, partially because I needed a break and partially because I wondered if thereâs anything in the statistics that would tell someone who didnât watch the match that the match is entirely on Coricâs racquet.  And looked at another way, if Coric had lost, could you look at his stats and know just how bad the loss is, because the match truly was on his racquet, and only he could blow it?
There are probably several ways to do this, and what Iâm presenting here is perhaps the most back-of-the-envelope way to do it, primarily because Iâm starting it on a whim at 11 pm while trying to stay interested in this match. So I think this is just a toy stat, although as I have posted before, I think toy stats have their own kind of value.
But âstatâ is the wrong word for the two things Iâm proposing here. âStatusâ is probably a better word...toy status(?). Both COM and LOS seek to identify particular matches, rather than producing a statistic for every match. Â
Iâm doing LOS first, because I was initially motivated by wondering how horrendous it would have been if Coric had lost this match when he was in total control of the match. Â
Lost Opportunity Score (LOS)Â
Iâm using the acronym LOS for this concept, but it is a bit of a misnomer because it isnât really a score. Nevertheless, the acronym is so apropos that I canât drop it. LOS should indicate when the match a player lost was almost entirely on his/her racquet and he/she blew it with too many errors.
Command of Match (COM)Â
We already have Carl Bialikâs Dominance Ratio (available for every match on Tennis Abstract), which indicates how much a player dominated the match statistically, but we donât know when that dominance is attributable to the winning player playing great, and when it is attributable to the losing player playing horribly. Â
COM is trying to identify when the winning player was in control, even when the other player did not play poorly.  In other words, COM isnât designed to measure how in command one player is (though I suppose you could use it for that), but rather, to identify those relatively rare matches where the match a player won was almost entirely on his/her racquet even thought his/her opponent may have played reasonably well.
Calculating LOS and COM
The fundamental basis for both LOS and COM is the same. For each player, calculate this number:
(1-(OppUEs/Points Won)) - (UEs/Points Played)
The first part of the formula determines what percentage of points won by the player were not gifts from the opponent. Some of those points may be unusual situations, but most of them will be winners or FEs caused by the player, and therefore within the playerâs control.  The second part of the formula indicates what percentage of overall points were gifts given away by the subject player.
Conceptually, if your first number is high, you were controlling the match to a significant degree, but if your second number also is high, you gave away a lot of points in a match.
To calculate LOS and COM, you need just one more step. Â
Lost Opportunity Score (LOS)
Divide the losing playerâs number by the winning playerâs number. If the losing playerâs quotient is greater than 1.10 (in other words, 10% higher), itâs a lost opportunity (LOS). In other words, the losing player had the match on his/her racquet, but made so many unforced errors that he/she gave the match away. The 10% buffer is to capture only the most egregious of these situations. It is approximately 1 standard deviation away from the average loser quotient.Â
Hereâs an example from the first round US Open match between Sam Stosur and Caroline Wozniacki, won by Wozniacki.  From the match score (6-3 6-2) it appears to be an easy win, and Wozâs dominance ratio was 1.56. Stosur won 45 out of 110 points.  She made 34 UEs and Wozniacki made just 12. Â
Stosurâs number via the formula above is (1-(12/45)) - (34/110) = .424
Wozâs number via the formula above is (1-(34/65) - (12/110) = .368
Then, .424/.368 = 1.15 (greater than 1.10), so Stosur gets a Lost Opportunity (LOS) âaward.â
Looking at the first part of the formula, Stosurâs points won were largely because of good things she was doing (73.3%), and Wozâs points won were mostly about Stosur doing bad things (47.7%). The second part shows Stosur made unforced errors on nearly 31% of points played, and Woz, typically, only 10.9%. Â Thatâs in keeping with what we know about their respective styles.
Bottom line: Stosur controlled the action in the match, but due in large part to the high number of UEs, lost the match. I suspect this is not uncommon for Wozniacki opponents. (See Caveats at the end).
Command of Match (COM)
Subtract the losing playerâs number from the winning playerâs number.  If the winning playerâs difference is greater than 0.13 for men, or 0.17 for women, the winning player had command of the match (COM).  In other words, the gap between how much control the winning player had, and how much control the losing player had, is so significant that we say the winning player was in command via his/her own efforts. Significantly, you can get a COM even if your opponent played reasonably well.
You might wonder where the 0.13 and 0.17 come from. Using US Open matches as the measuring stick, these numbers are 1.5 standard deviations from the mean differences between the players, so we are only capturing relatively rare matches with COM. I tried it with 2 SDs, but the list was far too thin.
Hereâs an example from the first round US Open match between Simona Halep and Kaia Kanepi, since most of us saw at least some part of that match and know there wasnât much Halep could do in that match. The score alone (6-2 6-4) gives us some indication of Kanepiâs level, and the dominance ratio was 1.36.  Halep won 47 out of 107 points, not that much different than in the Stosur example. Unlike Stosur, she made only 9 UEs and Kanepi made 28. Â
Halepâs number via the formula above is (1-(28/47)) - (9/107) = .320
Kanepiâs number via the formula above is (1-(9/60) - (28/107) = .588
Then, .588 - .320 = .268 (greater than .17), so Kanepi gets a Command of Match (COM) award.
Going back to our concept with the first part of the formula, Halepâs points won were largely because of bad things Kanepi was doing, with Halep controlling only 40% of those points. She didnât hurt herself with errors obviously. And because of that, only 15% of Kanepiâs successful points were due to her opponentâs mistakes. Â
Bottom line: Kanepi controlled the action in the match, to such a degree that even her significant number of errors, and Halepâs lack of errors, could not stop her. Â
Caveats
This is not scientific, so letâs get that out of the way. I havenât tested it on gobs and gobs of data.
Also, only 13 hours have passed since I first thought of the idea (and 7 of them were spent sleeping), so I reserve the right to make adjustments (or even scrap LOS and COM altogether).
I initially see three issues with LOS and COM:
1. UEs are not official statistics of the ATP and the WTA. They are typically recorded for the grand slams, although I noticed the IBM Slamtracker didnât bother with many lower profile matches at the US Open. Only 178 of the 254 US Open main draw matches had meaningful UE statistics. In the other 76 matches, IBM Slamtracker reported UEs, but they are clearly understated by vast amounts, so Iâm not sure why they even list them (or winners). For example, Andrey Rublev had only 5 winners and 13 unforced errors in a four set match, while his opponent Jeremy Chardy also had only 13 UEs? High-risk player Nikoloz Basilashvili had only 7 UEs in a five set match against Aljaz Bedene? I donât think so. Â
So, LOS and COM are good for only Grand Slams, matches that have been charted, or matches you are watching on TV that flash the summary numbers at the end of sets or matches. I donât feel too badly about this.
2. UEs are extremely subjective. Anyone who has charted a match and then seen the on-screen statistics from the TV broadcast knows the number of differences in judgment that can arise as to whether a player should have made the shot or not. Hopefully some of that is taken care of by the 10% buffer in the LOS calculation and the 1.5 standard deviation buffer in the COM calculation.
3. Aggressive players are far more likely to get a LOS or COM than steady players. Itâs not necessarily a bad thing in and of itself, so long as no one says âWozniacki has 0 COMs in 2018âł (if in fact she does have zero) and uses that as a stick to bash her with.Â
As a corollary, recognize that aggressiveness is just one way to measure who had control of the match. Steady play with few errors is arguably just as valid a way to keep the match on your own racquet, though it is a lot more subtle. Perhaps a player should get automatically get a COM if his or her opponent gets a LOS, but Iâm not yet convinced thatâs the right approach as it presumes the LOS players errors were mostly attributable to the steadiness of the opponent.Â
Since this one is so long, Iâll do another post with the list of LOS and COM awards from this yearâs US Open.

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Popcorn Scores: Quarterfinals
Here are the Popcorn Scores for the quarterfinal matches at St. Petersburg and at Metz, with red being the hottest, butteriest popcorn, grey being ordinary popcorn and blue being cold, stiff popcorn with very little salt.
I think Shapovalov v. Klizan could be more interesting than suggested here, depending on whether Klizan is âonâ or not. Nishikori v. Basilashvili also will be a baseline slugfest, although itâs hard to see Basilashvili being consistent enough to really be competitive.
Tennis Stuff I Didnât Know (about) Luca Vanni
Luca Vanni is a name frequently seen in Challenger events and qualifying draws, and it seems like he has been around forever. Now 33, he has held an ATP ranking since 2006, peaking at #100 in 2015. Hereâs a graph of his ranking progress (using a logarithmic scale):
Despite being a familiar name to those who watch a lot of tennis, he has only 22 ATP main draw matches under his belt (counting yesterdayâs loss in St. Petersburg). In those 22 contests, he has just 5 wins, and 3 of those came at Sao Paulo in 2015, where he made the final.
His 2015 Sao Paulo path to the final was a dream for a player like this. He qualified for the main draw, but before the qualifiers were placed, the tournamentâs top seed, Feliciano Lopez, withdrew from the tournament. The qualifiers and the lucky loser were thrown together for placement in the draw, and Vanni got Lopezâs spot, meaning he got a first round bye. Â
His second round opponent was Thiemo De Bakker, also a qualifier, who scraped by Juan Monaco in his first round match. Vanni defeated De Bakker in almost two hours, serving up 14 aces. Â
He avoided a seed in the quarterfinals, and defeated Dusan Lajovic in two tiebreaks (with double digit aces) to make the semis. He avoided the seed there too, when Joao Souza (not Sousa) knocked off Leonardo Mayer. That contest against Souza took nearly three hours, with Vanni again in double digit aces.
His easy run ended in the finals, where he got Pablo Cuevas, then ranked #30. He pushed him, losing in a third set tiebreak. Four matches, 47 aces, on clay. Not bad, and not the profile I expected for an Italian with only one career win on hard courts -- but he does say his idol was Marat Safin.  Did I mention he is 6â˛6âł (Vanni, I mean)?
Take 2015 Sao Paulo out, and he is 2-16 on the tour. If you look at his service profile, that W-L is surprising.  In his career, he has won 79% of his service games, which is a Top 50 number. Think Gael Monfils and Jack Sock. Unfortunately, he is required to return serve as well. He has managed to win only 11% of return games. Thatâs Karlovic/Isner/Muller territory, but those guys win 85%-95% of their service games. Â
In 12 years, Vanni has accumulated only about $675,000 in prize money. That works out to only about $56,000 per year, before travel, accommodation and other expenses. How does he do it? For one, he doesnât travel far very often. He lives in Foiano Della Chiana, Italy, which is between Florence and Rome in north-central Italy. In his 13 Challenger events this year, 10 were in Italy, France, Spain or Slovenia -- not more than two countries away. Another was in Glasgow, which is not as far from Italy as it seems. (The other two events were really far away, in Uzbekistan and Thailand.)
In 2018 Challenger events, he is 20-12 (plus 5-1 in Challenger qualifying matches), with one championship trophy and one other finals appearance. But he has made only 7 career appearances in Challenger finals (5-2), so heâs no Rendy Lu, or even Ricardas Berankis. That may have come as a surprise to Luca, because he won 16 Futures titles.