With the coming of championship season comes race projections!
The rankings and ratings from each race and each week are one aspect of the work that I do, this is another. These take those ratings and the variability from a runner’s resume, then apply that to a projection.
An explanation from previous years that still holds true:
Through a season’s worth of speed ratings, one can determine a runner’s average rating and the variability of those ratings. Both those factors are then weighed towards more recent meets, which are then simulated thousands of times using a Monte Carlo analysis (also described by TullyRunners here). Over those thousands of race simulations, one can determine a team’s average place and average score, a runner’s average place, odds on winning, and odds on qualifying.
These are useful in cross country scenarios, where you have consistent runners and those who are a bit more up and down. For example:
Tanner Time
Tanner Rating
Tanner Avg.
Tanner StDev.
15:30
210
15:45
205
16:00
200
16:15
195
16:30
190
200
7.91
MIXCSR Time
MIXCSR Rating
MIXCSR Avg.
MIXCSR StDev.
16:09
197
16:12
196
16:15
195
16:18
194
16:21
193
195
1.58
Jacob at his best might be faster than I in four of the five races during the season. I’d almost never be All-State in Division 1, but he might contend for a top-5 spot or place 60th.
If we did 20 simulations, this is how it might turn out:
Race
Tanner Avg.
Tanner StDev
Tanner Rating
MIXCSR Avg.
MIXCSR StDev
MIXCSR Rating
1
200
7.9
202.1
195
1.6
195.6
2
200
7.9
196.2
195
1.6
194.1
3
200
7.9
190.0
195
1.6
199.2
4
200
7.9
193.4
195
1.6
192.5
5
200
7.9
206.8
195
1.6
195.7
6
200
7.9
201.8
195
1.6
196.1
7
200
7.9
171.6
195
1.6
194.9
8
200
7.9
189.2
195
1.6
195.9
9
200
7.9
205.5
195
1.6
197.0
10
200
7.9
203.8
195
1.6
192.6
11
200
7.9
192.3
195
1.6
196.5
12
200
7.9
196.9
195
1.6
196.4
13
200
7.9
198.5
195
1.6
195.6
14
200
7.9
203.2
195
1.6
194.1
15
200
7.9
193.9
195
1.6
192.6
16
200
7.9
199.3
195
1.6
193.2
17
200
7.9
208.7
195
1.6
194.2
18
200
7.9
213.7
195
1.6
194.6
19
200
7.9
212.6
195
1.6
197.1
20
200
7.9
206.3
195
1.6
193.9
I’d win four times, but I’d never have the ability to win a title like he had in Race 18. He’s usually All-State, but may cost his team a title through his effort in Race 7.
The simulations give the possibilities of those scenarios and all the various scoring scripts. A sixth girl that can pop off on any certain day, a reliable boy who always runs 17-flat, you name it. Hopefully, they better elucidate our contenders, qualifiers, and how these races play out over the weekend.
At the regional level, the projections were pretty darn accurate. I gave qualifying odds for the top-3 spots and ended up thinking I’d be accurate on 198 of the teams, the final total ended up being 197. The State level was a different story, where a few wholly unexpected teams hit the podium.