2012 Miami Masters Projections

This week, the semifinals are swapped: If Federer is to continue his streak, he’ll need to go through Djokovic just to reach the final.  The most dangerous lower-ranked players seem to be well distributed throughout the draw: Del Potro would face Djokovic in the quarters; Roddick could hit Federer in the third; Raonic could face Murray in the third, and Isner is lurking in Nadal’s quarter.

If Fernando Gonzalez wants to make a run in his final tournament, he’ll have to go through Tomas Berdych in the second round–not exactly the easiest ask, even at the South American slam.

Here are the complete projections for the draw as it stands now, without qualifiers placed:

Player                        R64    R32    R16        W  
(1)Novak Djokovic          100.0%  82.4%  71.6%    22.5%  
Leonardo Mayer              17.5%   0.9%   0.2%     0.0%  
Marcos Baghdatis            82.5%  16.8%  10.6%     0.4%  
Qualifier1                  49.9%  16.7%   2.0%     0.0%  
Qualifier2                  50.1%  16.5%   1.9%     0.0%  
(27)Viktor Troicki         100.0%  66.8%  13.7%     0.3%  
                                                          
Player                        R64    R32    R16        W  
(17)Richard Gasquet        100.0%  67.7%  42.0%     0.9%  
Cedrik-Marcel Stebe         55.5%  19.2%   9.0%     0.1%  
Flavio Cipolla              44.5%  13.1%   5.5%     0.0%  
Albert Ramos-Vinolas        42.0%  10.7%   2.7%     0.0%  
Qualifier3                  58.0%  18.6%   5.9%     0.0%  
(15)Feliciano Lopez        100.0%  70.6%  34.9%     0.4%  
                                                          
Player                        R64    R32    R16        W  
(11)Juan Martin Del Potro  100.0%  79.0%  57.1%     6.0%  
Lukasz Kubot                45.8%   9.0%   3.7%     0.0%  
Ivo Karlovic                54.2%  12.0%   5.2%     0.0%  
Igor Kunitsyn               42.4%   9.1%   1.6%     0.0%  
(WC)Jesse Levine            57.6%  16.0%   3.3%     0.0%  
(23)Marin Cilic            100.0%  74.9%  29.1%     0.7%  
                                                          
Player                        R64    R32    R16        W  
(30)Julien Benneteau       100.0%  61.7%  24.6%     0.2%  
Benjamin Becker             47.4%  17.5%   5.1%     0.0%  
Olivier Rochus              52.6%  20.8%   6.1%     0.0%  
Sergiy Stakhovsky           34.2%  10.4%   4.9%     0.0%  
Bernard Tomic               65.8%  30.0%  19.0%     0.5%  
(5)David Ferrer            100.0%  59.6%  40.4%     1.4%  
                                                          
Player                        R64    R32    R16        W  
(3)Roger Federer           100.0%  84.8%  66.5%    12.4%  
(WC)Ryan Harrison           77.5%  13.9%   6.5%     0.1%  
Potito Starace              22.5%   1.4%   0.3%     0.0%  
Alex Bogomolov              54.9%  18.9%   3.7%     0.0%  
Gilles Muller               45.1%  13.6%   2.3%     0.0%  
(31)Andy Roddick           100.0%  67.5%  20.6%     0.6%  
                                                          
Player                        R64    R32    R16        W  
(21)Juan Monaco            100.0%  61.3%  23.9%     0.3%  
Yen-Hsun Lu                 43.1%  15.1%   3.9%     0.0%  
Jarkko Nieminen             56.9%  23.6%   7.5%     0.0%  
Qualifier4                  31.8%   6.2%   2.3%     0.0%  
Ernests Gulbis              68.2%  23.7%  13.1%     0.2%  
(14)Gael Monfils           100.0%  70.1%  49.3%     2.6%  
                                                          
Player                        R64    R32    R16        W  
(12)Nicolas Almagro        100.0%  64.3%  36.5%     0.7%  
Qualifier5                  38.3%  11.1%   4.1%     0.0%  
Donald Young                61.7%  24.6%  11.6%     0.1%  
Qualifier6                  66.9%  20.1%   6.7%     0.0%  
Carlos Berlocq              33.1%   5.8%   1.2%     0.0%  
(20)Fernando Verdasco      100.0%  74.1%  40.0%     0.7%  
                                                          
Player                        R64    R32    R16        W  
(28)Kevin Anderson         100.0%  54.9%  24.6%     0.4%  
Sam Querrey                 59.2%  28.9%  12.5%     0.2%  
Matthew Ebden               40.8%  16.3%   5.8%     0.0%  
Qualifier7                  37.2%   8.2%   2.7%     0.0%  
Jeremy Chardy               62.8%  20.6%   9.3%     0.1%  
(8)Mardy Fish              100.0%  71.2%  45.1%     2.0%  
                                                          
Player                        R64    R32    R16        W  
(7)Tomas Berdych           100.0%  83.6%  65.0%     5.0%  
Nicolas Mahut               85.8%  15.9%   7.3%     0.0%  
(WC)Fernando Gonzalez       14.2%   0.5%   0.1%     0.0%  
Grigor Dimitrov             47.7%  35.6%  11.0%     0.1%  
Mikhail Kukushkin           52.3%  39.8%  13.2%     0.1%  
(29)Juan Ignacio Chela     100.0%  24.5%   3.4%     0.0%  
                                                          
Player                        R64    R32    R16        W  
(18)Alexandr Dolgopolov    100.0%  69.7%  35.0%     0.8%  
Qualifier8                  43.4%  11.8%   3.3%     0.0%  
(WC)Denis Kudla             56.6%  18.5%   6.2%     0.0%  
David Nalbandian            66.0%  34.0%  19.7%     0.6%  
Steve Darcis                34.0%  11.9%   4.9%     0.0%  
(9)Janko Tipsarevic        100.0%  54.1%  30.8%     0.8%  
                                                          
Player                        R64    R32    R16        W  
(13)Gilles Simon           100.0%  66.6%  41.5%     0.9%  
Qualifier9                  36.5%   9.4%   3.5%     0.0%  
Andreas Seppi               63.5%  24.0%  12.2%     0.1%  
Robin Haase                 58.0%  24.0%   9.3%     0.0%  
(WC)Marinko Matosevic       42.0%  14.0%   4.4%     0.0%  
(22)Jurgen Melzer          100.0%  62.0%  29.1%     0.3%  
                                                          
Player                        R64    R32    R16        W  
(26)Milos Raonic           100.0%  69.3%  23.2%     0.8%  
Dudi Sela                   59.0%  20.0%   4.3%     0.0%  
Qualifier10                 41.0%  10.7%   1.7%     0.0%  
Alejandro Falla             42.5%   6.0%   2.2%     0.0%  
Denis Istomin               57.5%  10.7%   4.6%     0.1%  
(4)Andy Murray             100.0%  83.3%  64.0%    12.0%  
                                                          
Player                        R64    R32    R16        W  
(6)Jo-Wilfried Tsonga      100.0%  80.8%  57.6%     4.5%  
Qualifier11                 43.8%   7.4%   2.5%     0.0%  
Xavier Malisse              56.2%  11.8%   4.8%     0.0%  
Thomaz Bellucci             70.8%  28.9%   9.0%     0.1%  
Frederico Gil               29.2%   6.5%   1.1%     0.0%  
(32)Philipp Kohlschreiber  100.0%  64.5%  24.9%     0.4%  
                                                          
Player                        R64    R32    R16        W  
(19)Florian Mayer          100.0%  60.2%  27.7%     0.6%  
Philipp Petzschner          45.4%  17.0%   5.9%     0.0%  
Ivan Dodig                  54.6%  22.7%   8.7%     0.1%  
Nikolay Davydenko           59.5%  18.7%   8.4%     0.1%  
James Blake                 40.5%   9.7%   3.5%     0.0%  
(10)John Isner             100.0%  71.6%  45.8%     2.2%  
                                                          
Player                        R64    R32    R16        W  
(16)Kei Nishikori          100.0%  70.2%  44.6%     1.7%  
Ryan Sweeting               42.7%  11.3%   4.4%     0.0%  
Lukas Lacko                 57.3%  18.5%   8.4%     0.1%  
Michael Llodra              63.8%  27.7%  11.2%     0.1%  
Lukas Rosol                 36.2%  11.0%   3.1%     0.0%  
(24)Marcel Granollers      100.0%  61.3%  28.3%     0.4%  
                                                          
Player                        R64    R32    R16        W  
(25)Radek Stepanek         100.0%  64.8%  13.3%     0.1%  
Qualifier12                 75.1%  30.6%   4.8%     0.0%  
Tommy Haas                  24.9%   4.6%   0.3%     0.0%  
Pablo Andujar               28.6%   2.3%   0.9%     0.0%  
Santiago Giraldo            71.4%  12.7%   7.4%     0.1%  
(2)Rafael Nadal            100.0%  84.9%  73.3%    14.0%
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11 Comments

Filed under Forecasting, Miami

11 responses to “2012 Miami Masters Projections

  1. Do you also take into account the player’s current form and the Head to head of players?

    • More recent matches are weighted more heavily, yes. There’s a small H2H component, but my research has indicated that H2H doesn’t add very much accuracy to the projections.

  2. Daniel Pino

    Looking at your predictions, I’d like to ask you something. Your algorithm takes into account the previous results of many matches, including the recent ones as well as the older. So my question is, Don’t you think that adding a bigger weight to the recent matches could lead to better predictions?

    I mean, when you look at the way some top players are playing right now, for example, Mardy Fish, I really don’t think the probability he has of winning the tournament is that big (~2%) …

    I think Nabaldian would have more changes of qualifying to the R32 than Tipsarevic, however your predictions predicts otherwise (Tipsarevic has 54.2% of probability of qualifying to R32 against 34% of Nalbandian)…

    Maybe I’m wrong, but don’t you think that this could be like that because your algorithm doesn’t give a bigger weight to the latest result ??

    • Daniel Pino

      Sorry, when my message was posted the other wasn’t there…

    • My algorithm does weight more recent matches more heavily. I tested a variety of weights to come up with what most accurate predicted outcomes in the past.

      However, humans tend to weight more recent events way TOO heavily. We see one bad match from Fish (or Murray, or whomever) and think that he must be out of form … my system recognizes that players have blips like that, and while they tell us something, they don’t tell us that much, compared to their record over a span of a year or two.

      • OK, I’ll put up a completely ignorant question – does this mean you are in effect calculating variance for each player? And if so is it in effect a “moving variance”? E.g. if I were a poker player trying to calculate my expectation for a given game (let’s say $5/$10 limit hold’em), typically I would need to decide what period I chose. With poker, variance is extremely high, so the longer the period the better, thus I might include literally all my results going X many years back, even if I think I’ve become a better player in the last couple of years. With tennis players, what seems to work best?

        Probably though I’ve got this all wrong . . .

      • Daniel Pino

        I think I got your point. However I think that the recent events should be heavier when trying to predict the result of a match between players nearly ranked.

        I mean, looking at the way Federer and Nadal are playing, I wouldn’t be surprised to see Federer defeat Nadal again, if they both get to the final…

      • “I mean, looking at the way Federer and Nadal are playing, I wouldn’t be surprised to see Federer defeat Nadal again, if they both get to the final…”

        The problem with this is it’s weighting a single result as if the outcome (Federer winning at Indian Wells) were expressive of a trend. A single result is not a trend, it’s a single result, even if it’s recent. Subjectively we may have thoughts such as “Ah, Fed beat Nadal, he’s hot and Nadal is not.” But that doesn’t translate into any sort of algorithm that could ever hold up.

        Along the same lines, I was dipping recently into a book for laypeople by the cognitive psychologist and reseacher Daniel Kahneman, and he mentioned that experts often go wrong in decision making by under-weighting statistical evidence and over-weighting their own ability to subjectively interpret individual events as having been caused by complex factors they feel they have insight into. Another way of saying this is, complex problems are often not best solved by complex methods – they can be better solved (in the long run especially) by simple methods.

        If we actually try to predict a tennis match by drawing out the many complex factors that go into it, we may not actually make the problem easier to solve – we may instead make it harder. One reason is, we have no method for systematically weighting the individual factors. Take the Indian Wells final: Nadal specifically said in his presser afterwards that the unexpected cold temperatures had caused the ball to bounce lower, favoring the Federer backhand over Nadal’s topspin strokes. What weight can we possibly assign this factor? We don’t really know. Common sense says Miami probably won’t experience chilling weather the way Indio, California did – but then, how much of Federer’s win at Indian Wells do we assign to the cold-weather factor, versus possibly something else at work? And then how could you ever duplicate this level of analysis in an algorithm that is intended to handle all players?

      • @wholesight — I’m not calculating variance, though some of what you say applies. I’m calculating the best approximation of the true current talent level of the player, factoring in everything we can about the environment.

        AFAICT, the last two years are most relevant, and yesterday’s matches are roughly twice as relevant as matches 365 days ago, which are roughly twice as relevant 365 days before that.

        @Daniel: As I said, I *do* weight more recently matches more heavily. And what you “think” should be done (or what I think should be done, or what anyone thinks) isn’t relevant here — the weighting formula I use is the best one I’ve found at predicting future results, between closely matched players or not-so-closely matched players.

        @wholesight (2): those are good points about having too much information. From a practical perspective, I’m not going to bother including temperature in my database. I don’t have the data to know who had to play back-to-back days, or who played on center court, or who was battling the flu at Indian Wells, and so on. Maybe a skilled and experienced bettor (or bookie) could shade lines appropriately in some of those cases (I would certainly give Gonzo better odds this week than my algorithm does), but in general, much more info is too much info.

  3. Tony Moffitt

    I have the same question that still has not been answered. (see your recent hardcourt rankings article).
    Do you plan on regularly publishing a rankings list (eg every month or so) or are the rankings lists you have published previously centred around the slams? A list on a regular basis would be a wonderful, useful tool for people who like a little bet :)

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