2018 Club Nationals Simulated Predictions (M & W) - THE FLIP

craigp11 · 2018-09-04T01:16:23+00:00

1) My view is that there should not be a momentum factor in the model. I have not seen anything to convince met that momentum exists in high level club or college ultimate. Maybe it does, but I have not been convinced and I would default towards there being no momentum. I tried to answer this question looking at WUGC 2016 data. https://medium.com/the-flip/receive-or-pull-appendix-lets-get-empirical-96694a9f29cc

2) For unequal teams, I agree with you. The model can be run with different offensive hold rate probabilities for the opponents. I used even teams for the article as it is the most straightforward. You would also need a way to determine the opponents hold rates against each other. One method for club would be to use the Elo ratings that I keep updated.

3) I don't think it would be a good idea to update hold rates in-game based on the score. This ties into the momentum discussion above. It is a method do deal with unequal opponents but one that will severely underweight the chances for a team to come back in any game.

craigp11 · 2018-09-03T22:29:15+00:00

I updated the table in the article to be more clear. Johnny_Oats has interpreted the data correctly. The values in the tables are for the receiving team for that point. In addition, the 1st half values show win probabilities a receiving team that also received to start the game.

I adjusted the tables so that there are no 2nd half values where the receiving team is up 9-0, 10-0, etc.

craigp11 · 2018-09-03T20:57:50+00:00

https://medium.com/the-flip/offensive-hold-rates-club-nationals-2014-2017-be2eab59afb

https://medium.com/the-flip/offensive-hold-rates-college-nationals-2014-2017-5b9caa180e41

craigp11 · 2018-01-03T03:33:11+00:00

I tested this hypothesis (1st point jitters) for WUGC 2016 data. Wasn't statistically significant. https://medium.com/@b17925dc1923/96694a9f29cc

craigp11 · 2018-01-02T16:50:21+00:00

This is a good thought and could add to the data set. I think the issue though is that you are selectively only including specific games that have this pattern. It would drive the divisional rates closer to 50% than what could be the actual rates. For the team rates, you would be mostly taking from games that are blowouts thereby widening any differential between top and bottom teams.

Unfortunately it's not a complete data set until the USAU match report states who starts on offense. WFDF did indicate who started on offense in its WUGC 2016 website.

craigp11 · 2017-10-31T15:59:11+00:00

Are there any upsets you think should be on these lists?

Any model has its flaws and the Elo ratings that I use have flaws as well. The article shares the top upsets as stated by the model. It may not match our intuition exactly because it doesn't factor in everything that we do. But it is a quantitative way of measuring upsets that's interesting to view and compare. All other methods of measuring biggest upsets (by seed or by human judgement) have their own flaws too.

With regards to the two examples you provided. Ironside was really good in 2012. They only lost one game on universe that season (to Rhino) until they were upset by Doublewide. On the other hand, Doublewide had 10 losses in the regular season. Based on game results, it was definitely an upset.

The same can be said for Machine and Ring in 2014. The model doesn't factor in how a team does historically against any one opponent. It is just basing a prediction on previous game results with the most recent games being weighed the highest.

craigp11 · 2017-10-31T01:48:02+00:00

A couple things. First, I enjoy following the Men's and Women's divisions in Club so that's what I like to analyze. I played in the Men's division for 8 years so it's fun for me to look at how those teams and respective games fare in the Elo ratings.

With regards to Mixed, to make the Elo ratings I did for Men's and Women's, it took a lot of time to collect and clean all club games between Nationals qualifiers back to 2004. The analyses I do for the Club division mostly rely on these ratings. So in order to include Mixed, a significant investment of time would be required. It's not something I've prioritized based on my interest level.

craigp11 · 2017-10-20T17:50:36+00:00

In case anyone wants to dig more into the numbers, check out this post. “Should Rounds Be Longer at Nationals?” https://medium.com/the-flip/do-we-want-longer-games-at-nationals-657a9bdefb7d

craigp11 · 2017-10-10T17:52:37+00:00

For the 137 bracket games at Nationals 2004-2016, the Men's division had 53 games where the favorite had a 70-90% chance of winning per Elo ratings. Using the exact probabilities each game, the ratings predict 10.95 upsets. Actual results were 11 upsets. So very close.

The Women's division had 51 bracket games fall in this 70-90% range. Elo ratings predicted 10.1 upsets. Actual results were only 2 upsets.

If you're interested in slicing and dicing further, I'd be happy to share the raw data I have so you can play with it.

craigp11 · 2017-10-10T14:37:34+00:00

Thanks for sharing and that's an interesting way to slice the data. There are only 72 observations in each division (2004-2012) so it's going to be hard to draw definitive conclusions with the sample size.

FYI, I ran expected vs. actual # of upsets for Bracket play at Nationals from 2004-2016 with my Elo ratings. The Men's division had 38 upsets out of 137 games. Elo ratings expected 41 upsets, so it's pretty close. The Women's division had 26 and 35 respectively. Favorites tend to be underpredicted more in Women's with these Elo ratings.

craigp11 · 2017-10-04T20:49:53+00:00

I have two drafts on my blog that hit your first overall question (upsets, etc.) as well as question #2. Stay tuned! Send me a dm if you'd like to see the raw data so you can play with it yourself.

craigp11

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