A look at who has a chance to finish well this weekend
May 17, 2018 by Aaron Howard in Analysis with 0 comments
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.
| Player | Elo Rating | Change In Elo | Win Prob. | Top-5 Prob. | Top-10 Prob. |
|---|---|---|---|---|---|
| Paul McBeth | 2021.5 | 23.4 | 41.1 | 88.3 | 94.3 |
| Richard Wysocki | 2030.2 | 16.8 | 28.6 | 81.4 | 90.9 |
| Jeremy Koling | 1806.6 | 12.4 | 4.2 | 32.6 | 54.2 |
| Nathan Doss | 1800.1 | 11.7 | 3.7 | 29.9 | 51.2 |
| Nathan Sexton | 1791.7 | 11.4 | 3.3 | 28.0 | 49.0 |
| Philo Brathwaite | 1774.7 | 8.7 | 2.2 | 20.8 | 39.9 |
| Drew Gibson | 1728.5 | 7.0 | 1.3 | 13.5 | 28.5 |
| Paul Ulibarri | 1800.2 | 0.0 | 1.1 | 12.6 | 27.7 |
| Simon Lizotte | 1782.2 | 0.0 | 1.0 | 11.1 | 24.9 |
| Dustin Keegan | 1633.1 | 9.8 | 0.8 | 8.5 | 19.2 |
| Eagle McMahon | 1747.5 | 0.0 | 0.7 | 8.6 | 20.1 |
| Austin Turner | 1608.6 | 10.9 | 0.7 | 7.8 | 17.8 |
| Kyle Crabtree | 1561.3 | 14.0 | 0.7 | 7.2 | 16.3 |
| Gregg Barsby | 1694.1 | 0.0 | 0.5 | 5.8 | 14.2 |
| Scott Withers | 1551.1 | 10.5 | 0.4 | 4.9 | 11.8 |
| Manabu Kajiyama | 1550.9 | 10.1 | 0.4 | 4.7 | 11.4 |
| Eric Oakley | 1592.8 | 5.8 | 0.4 | 4.5 | 11.0 |
| Nate Perkins | 1571.3 | 5.7 | 0.3 | 3.7 | 9.3 |
| James Conrad | 1630.5 | 0.0 | 0.3 | 3.5 | 9.1 |
| A.J. Risley | 1611.7 | 1.3 | 0.3 | 3.4 | 8.8 |
| Seppo Paju | 1626.8 | 0.0 | 0.3 | 3.4 | 8.9 |
| Avery Jenkins | 1622.5 | -0.2 | 0.3 | 3.3 | 8.4 |
| Peter McBride | 1615.8 | 0.0 | 0.2 | 3.2 | 8.2 |
| Robert Lockwood | 1523.2 | 7.3 | 0.2 | 3.0 | 7.5 |
| Kyle Eckmann | 1542.6 | 4.8 | 0.2 | 2.8 | 7.1 |
| Sias Elmore | 1534.3 | 4.6 | 0.2 | 2.5 | 6.6 |
| Max Nichols | 1588.6 | 0.3 | 0.2 | 2.6 | 6.9 |
| Noah Meintsma | 1535.5 | 4.1 | 0.2 | 2.5 | 6.3 |
| Lewis Bitney | 1541.5 | 3.4 | 0.2 | 2.4 | 6.3 |
| Matt Bell | 1568.6 | 0.0 | 0.2 | 2.2 | 5.8 |
| Kevin Jones | 1566.0 | 0.0 | 0.2 | 2.1 | 5.7 |
| Brian Cole | 1501.0 | 4.7 | 0.2 | 2.0 | 5.1 |
| Grady Shue | 1559.9 | 0.0 | 0.2 | 2.0 | 5.4 |
| Austin Hannum | 1555.3 | 0.0 | 0.2 | 2.0 | 5.2 |
| Joshua Anthon | 1554.7 | 0.0 | 0.2 | 2.0 | 5.2 |
| Colten Montgomery | 1552.6 | 0.0 | 0.1 | 1.9 | 5.1 |
| Garrett Gurthie | 1550.7 | 0.0 | 0.1 | 1.9 | 5.1 |
| Nick Wood | 1548.7 | 0.0 | 0.1 | 1.9 | 5.0 |
| Alex Russell | 1548.1 | 0.0 | 0.1 | 1.9 | 5.0 |
| Ruben Alaniz | 1496.8 | 3.3 | 0.1 | 1.7 | 4.4 |
| Lance Brown | 1537.9 | 0.0 | 0.1 | 1.7 | 4.6 |
| Andrew Nava | 1513.9 | 1.7 | 0.1 | 1.7 | 4.4 |
| Sean Kapalko | 1505.8 | 2.0 | 0.1 | 1.6 | 4.3 |
| Christopher Watson | 1522.2 | 0.0 | 0.1 | 1.5 | 4.1 |
| Jesse Bickley | 1520.4 | 0.0 | 0.1 | 1.5 | 4.0 |
| Bradley Williams | 1517.8 | 0.0 | 0.1 | 1.5 | 4.0 |
| Landon Knight | 1514.0 | 0.0 | 0.1 | 1.4 | 3.8 |
| Luis Nava | 1512.9 | 0.0 | 0.1 | 1.4 | 3.8 |
| Cameron Sheehan | 1512.0 | 0.0 | 0.1 | 1.4 | 3.8 |
| Nick Newton | 1511.3 | 0.0 | 0.1 | 1.4 | 3.8 |
| Connor Hanrahan | 1508.2 | 0.0 | 0.1 | 1.4 | 3.7 |
| Eli Grijalva | 1507.0 | 0.0 | 0.1 | 1.3 | 3.6 |
| Nathan Ryan | 1506.8 | 0.0 | 0.1 | 1.3 | 3.6 |
| Shaun Kirk | 1506.4 | 0.0 | 0.1 | 1.3 | 3.6 |
| Mike Sale | 1505.5 | 0.0 | 0.1 | 1.3 | 3.6 |
| Thomas Tomaselli | 1505.2 | 0.0 | 0.1 | 1.3 | 3.6 |
| Bryan Peterson | 1503.4 | 0.0 | 0.1 | 1.3 | 3.5 |
| Andrew McGill | 1502.9 | 0.0 | 0.1 | 1.3 | 3.5 |
| Ian Chadwick | 1502.8 | 0.0 | 0.1 | 1.3 | 3.5 |
| Dylan Evans | 1502.6 | 0.0 | 0.1 | 1.3 | 3.5 |
| Trevor Parker | 1501.7 | 0.0 | 0.1 | 1.3 | 3.5 |
| Alan Wagner | 1501.3 | 0.0 | 0.1 | 1.3 | 3.5 |
| David Madruga | 1501.0 | 0.0 | 0.1 | 1.3 | 3.5 |
| Samuel Aldrich | 1500.0 | 0.0 | 0.1 | 1.3 | 3.5 |
| Andrew Bailey | 1500.0 | 0.0 | 0.1 | 1.3 | 3.5 |
| Armando Ensenat | 1500.0 | 0.0 | 0.1 | 1.3 | 3.5 |
| Gabriel Luedecke | 1500.0 | 0.0 | 0.1 | 1.3 | 3.5 |
| Patrick McNett | 1500.0 | 0.0 | 0.1 | 1.3 | 3.5 |
| Travis Powell | 1500.0 | 0.0 | 0.1 | 1.3 | 3.5 |
| Logan Riding | 1500.0 | 0.0 | 0.1 | 1.3 | 3.5 |
| Nicholas Spitler | 1500.0 | 0.0 | 0.1 | 1.3 | 3.5 |
| Zoltan Szemeredi | 1500.0 | 0.0 | 0.1 | 1.3 | 3.5 |
| Erik Thomas | 1500.0 | 0.0 | 0.1 | 1.3 | 3.5 |
| Wes Morrison | 1499.9 | 0.0 | 0.1 | 1.3 | 3.5 |
| Richard Little | 1499.8 | 0.0 | 0.1 | 1.3 | 3.4 |
| D.J. Cookson | 1499.7 | 0.0 | 0.1 | 1.3 | 3.4 |
| Aaron Konichek | 1498.8 | 0.0 | 0.1 | 1.3 | 3.4 |
| Jack Lu | 1498.2 | 0.0 | 0.1 | 1.3 | 3.4 |
| Mike Westlund | 1498.1 | 0.0 | 0.1 | 1.3 | 3.4 |
| Cedar Morgan | 1497.3 | 0.0 | 0.1 | 1.2 | 3.4 |
| Lance Landgren | 1496.7 | 0.0 | 0.1 | 1.2 | 3.4 |
| Ryan Dickson | 1503.6 | -1.1 | 0.1 | 1.2 | 3.2 |
| Jeff Faes | 1507.7 | -1.9 | 0.1 | 1.1 | 3.1 |
| Player | Elo Rating | Change In Elo | Win Prob. | Top-5 Prob. | Top-10 Prob. |
|---|---|---|---|---|---|
| Paige Pierce | 1731.0 | 16.7 | 46.1 | 99.8 | 99.8 |
| Catrina Allen | 1673.7 | 7.7 | 28.2 | 94.0 | 98.1 |
| Sarah Hokom | 1634.5 | 7.0 | 12.3 | 81.9 | 94.4 |
| Valarie Jenkins | 1594.9 | 11.9 | 4.1 | 77.7 | 89.6 |
| Lisa Fajkus | 1577.6 | 0.0 | 1.9 | 20.1 | 67.0 |
| Jessica Weese | 1573.9 | 2.2 | 1.8 | 25.0 | 68.7 |
| Jennifer Allen | 1565.2 | 12.0 | 1.5 | 59.9 | 79.3 |
| Madison Walker | 1551.6 | 2.9 | 0.8 | 16.8 | 55.9 |
| Zoe Andyke | 1538.2 | 8.1 | 0.6 | 25.7 | 57.3 |
| Paige Bjerkaas | 1528.2 | 3.2 | 0.4 | 9.7 | 40.6 |
| Melody Waibel | 1527.4 | 0.0 | 0.4 | 5.6 | 34.1 |
| Nicole Bradley | 1521.2 | 7.1 | 0.3 | 15.0 | 43.7 |
| Kona Star Panis | 1513.8 | 0.0 | 0.2 | 3.9 | 26.3 |
| Lesli Todd | 1507.1 | 0.2 | 0.2 | 3.3 | 23.2 |
| Vanessa Van Dyken | 1506.2 | 0.0 | 0.2 | 3.1 | 22.5 |
| Antara De Bourbon | 1500.0 | 0.0 | 0.1 | 2.6 | 19.7 |
| Candace Romaine | 1500.0 | 0.0 | 0.1 | 2.6 | 19.7 |
| Kelly Muth | 1499.6 | -1.1 | 0.1 | 2.2 | 18.2 |
| Amy Lewis | 1499.4 | -5.3 | 0.1 | 1.0 | 13.6 |
| Anni Kreml | 1499.1 | 9.2 | 0.2 | 11.9 | 33.5 |
| Erika Stinchcomb | 1498.6 | -0.2 | 0.1 | 2.4 | 18.8 |
| Jennifer Morgan | 1497.9 | -2.1 | 0.1 | 1.7 | 16.3 |
| Shinah Kim | 1488.7 | 0.0 | 0.1 | 1.9 | 15.3 |
Take a look at the full probability tables at the bottom of the article. ↩
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. ↩
As quantified using the cross-validation method. ↩
For example, the baseball forecasting model PECOTA has been around for 15 years and is still updated periodically. ↩
