Masters Cup Win Probabilities And GBO Review

A look at who has a chance to finish well this weekend

Paul McBeth putting at DeLaveaga, site of the Masters Cup. Photo: PDGA

The 2018 Santa Cruz Masters Cup starts today, and like with the Glass Blown Open last month, I am sharing the win, top-5, and top-10 probabilities for the Open and Open Women’s divisions.

Once again, these probabilities are based on Elo ratings and those with the best chances to win the Masters Cup are not very surprising. Ricky Wysocki now has the highest Elo rating in the Open division, but Paul McBeth has the highest win probability. This is in a large part based upon his performance last year, which included an unbelievable 1100 rated round. On the Open Women’s side, the model picks Paige Pierce followed by Catrina Allen, and Sarah Hokom. Not much else needs to be said about Pierce’s dominance as of late.1

Looking back, how did the model do with this year’s Glass Blown Open? On the Open Women’s side, it did pretty well, getting the winner correct and seven out of the top-10. On the Open side, it didn’t do quite as well. It correctly picked Wysocki in second place, but only got four out of the top-10. One reason that the model didn’t do as well in the Open division is because of the high amount of parity in the division this year, and likely the changes in courses. Yes, Eagle McMahon has been playing well this season and has won two tournaments, but there have also been three other winners at NT and DGPT events (Simon Lizotte, Jeremy Koling, and Wysocki). At this time in 2016 and 2017, there were only two winners (in 2016 it was Wysocki and Cameron Todd; in 2017 it was Wysocki and McBeth). Further, there were only six different winners of NT and DGPT events all of last year.2 We are already two-thirds of the way to that number this year, and its only May.

All of this means that the model, which makes predictions based on results from events all the way back to 2012, is trying to catch up with this relatively unprecedented amount of uncertainty. To help the model deal with this, I have tweaked it a bit by weighting tournaments in the model based on year. Tournaments that occurred in 2018 are weighted more than those from 2017, and so on. This did help improve the predictive power of the model,3 but only by a few percentage points. This model, like most others, is a continuing work in progress and will (hopefully) only get better.4

Besides the favorites to win, others to keep an eye on for the Masters Cup are Kyle Crabtree and Manabu Kajiyama. Neither of these players enter many events, but when they do they finish well. Both finished in the top-10 at the Masters Cup last year. Elsewhere, keep an eye on Zoe Andyke, she finished very strong last year (963 rated 3rd round) and is predicted to be in the top-10 this year. Take a closer look at the probabilities and let us know in the comments if you notice anything interesting!


The tables below show sortable columns containing: current Elo rating, change in Elo rating due to 2017 Masters Cup, and Win, Top-5, and Top-10 probabilities (as percentages) for the 20 players in each division with the highest win probabilities. For players that have not played in any NT or DGPT events this year or last, their Elo ratings were set at 1500, which is the value I use to start estimating a rating for any player.

PlayerElo RatingChange In EloWin Prob.Top-5 Prob.Top-10 Prob.
Paul McBeth2021.523.441.188.394.3
Richard Wysocki2030.216.828.681.490.9
Jeremy Koling1806.612.44.232.654.2
Nathan Doss1800.111.73.729.951.2
Nathan Sexton1791.711.43.328.049.0
Philo Brathwaite1774.78.72.220.839.9
Drew Gibson1728.57.01.313.528.5
Paul Ulibarri1800.20.01.112.627.7
Simon Lizotte1782.20.01.011.124.9
Dustin Keegan1633.19.80.88.519.2
Eagle McMahon1747.50.00.78.620.1
Austin Turner1608.610.90.77.817.8
Kyle Crabtree1561.314.00.77.216.3
Gregg Barsby1694.10.00.55.814.2
Scott Withers1551.110.50.44.911.8
Manabu Kajiyama1550.910.10.44.711.4
Eric Oakley1592.85.80.44.511.0
Nate Perkins1571.35.70.33.79.3
James Conrad1630.50.00.33.59.1
A.J. Risley1611.71.30.33.48.8
Seppo Paju1626.80.00.33.48.9
Avery Jenkins1622.5-0.20.33.38.4
Peter McBride1615.80.00.23.28.2
Robert Lockwood1523.27.30.23.07.5
Kyle Eckmann1542.64.80.22.87.1
Sias Elmore1534.34.60.22.56.6
Max Nichols1588.60.30.22.66.9
Noah Meintsma1535.54.10.22.56.3
Lewis Bitney1541.53.40.22.46.3
Matt Bell1568.60.00.22.25.8
Kevin Jones1566.00.00.22.15.7
Brian Cole1501.04.70.22.05.1
Grady Shue1559.90.00.22.05.4
Austin Hannum1555.30.00.22.05.2
Joshua Anthon1554.70.00.22.05.2
Colten Montgomery1552.60.00.11.95.1
Garrett Gurthie1550.70.00.11.95.1
Nick Wood1548.70.00.11.95.0
Alex Russell1548.10.00.11.95.0
Ruben Alaniz1496.83.30.11.74.4
Lance Brown1537.90.00.11.74.6
Andrew Nava1513.91.70.11.74.4
Sean Kapalko1505.82.00.11.64.3
Christopher Watson1522.20.00.11.54.1
Jesse Bickley1520.40.00.11.54.0
Bradley Williams1517.80.00.11.54.0
Landon Knight1514.00.00.11.43.8
Luis Nava1512.90.00.11.43.8
Cameron Sheehan1512.00.00.11.43.8
Nick Newton1511.30.00.11.43.8
Connor Hanrahan1508.20.00.11.43.7
Eli Grijalva1507.00.00.11.33.6
Nathan Ryan1506.80.00.11.33.6
Shaun Kirk1506.40.00.11.33.6
Mike Sale1505.50.00.11.33.6
Thomas Tomaselli1505.20.00.11.33.6
Bryan Peterson1503.40.00.11.33.5
Andrew McGill1502.90.00.11.33.5
Ian Chadwick1502.80.00.11.33.5
Dylan Evans1502.60.00.11.33.5
Trevor Parker1501.70.00.11.33.5
Alan Wagner1501.30.00.11.33.5
David Madruga1501.00.00.11.33.5
Samuel Aldrich1500.00.00.11.33.5
Andrew Bailey1500.00.00.11.33.5
Armando Ensenat1500.00.00.11.33.5
Gabriel Luedecke1500.00.00.11.33.5
Patrick McNett1500.00.00.11.33.5
Travis Powell1500.00.00.11.33.5
Logan Riding1500.00.00.11.33.5
Nicholas Spitler1500.00.00.11.33.5
Zoltan Szemeredi1500.00.00.11.33.5
Erik Thomas1500.00.00.11.33.5
Wes Morrison1499.90.00.11.33.5
Richard Little1499.80.00.11.33.4
D.J. Cookson1499.70.00.11.33.4
Aaron Konichek1498.80.00.11.33.4
Jack Lu1498.20.00.11.33.4
Mike Westlund1498.10.00.11.33.4
Cedar Morgan1497.30.00.11.23.4
Lance Landgren1496.70.00.11.23.4
Ryan Dickson1503.6-1.10.11.23.2
Jeff Faes1507.7-1.90.11.13.1
PlayerElo RatingChange In EloWin Prob.Top-5 Prob.Top-10 Prob.
Paige Pierce1731.016.746.199.899.8
Catrina Allen1673.77.728.294.098.1
Sarah Hokom1634.57.012.381.994.4
Valarie Jenkins1594.911.94.177.789.6
Lisa Fajkus1577.60.01.920.167.0
Jessica Weese1573.92.21.825.068.7
Jennifer Allen1565.212.01.559.979.3
Madison Walker1551.62.90.816.855.9
Zoe Andyke1538.28.10.625.757.3
Paige Bjerkaas1528.23.20.49.740.6
Melody Waibel1527.40.00.45.634.1
Nicole Bradley1521.27.10.315.043.7
Kona Star Panis1513.80.00.23.926.3
Lesli Todd1507.10.20.23.323.2
Vanessa Van Dyken1506.20.00.23.122.5
Antara De Bourbon1500.00.00.12.619.7
Candace Romaine1500.00.00.12.619.7
Kelly Muth1499.6-1.10.12.218.2
Amy Lewis1499.4-5.30.11.013.6
Anni Kreml1499.19.20.211.933.5
Erika Stinchcomb1498.6-0.20.12.418.8
Jennifer Morgan1497.9-2.10.11.716.3
Shinah Kim1488.70.00.11.915.3

  1. Take a look at the full probability tables at the bottom of the article. 

  2. I mentioned this parity issue in my GBO probabilities article, and it is an interesting phenomenon that I want to investigate in more detail at some point. 

  3. As quantified using the cross-validation method. 

  4. For example, the baseball forecasting model PECOTA has been around for 15 years and is still updated periodically. 

  1. Aaron Howard
    Aaron Howard

    Aaron Howard is a Visiting Assistant Professor at Franklin & Marshall College. He loves to play disc golf and to think about things he loves quantitatively. Contact him at ahoward1@fandm.edu and follow him on Instagram.

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