BY CHARLES SCUDDER | @cscudder
Unlike Quals, Team Pursuit results can yield a decent model for predicting race day success. Team Pursuit and Miss ‘N Out are better simulators for how teams perform on the day of the race than the other events before race day.
IUSF records are inconsistent at best when it comes to Miss ‘N Out results, giving us an uphill battle when creating a historic base on which to extract winning probability. Instead, we can look at the past 20 years of Team Pursuit results to predict the chances teams have of winning based exclusively on their Team Pursuit placement.
MEN
1. Black Key Bulls — 38.89%
2. Phi Gamma Delta — 5.56%
3. Cutters — 27.78%
4. Phi Delta Theta — 11.11%
5. Delta Tau Delta — 5.56%
6. Beta Theta Pi — 5.56%
7. Sigma Alpha Epsilon — 0%
8. Wright Cycling — 0%
9. CSF — 0%
10. Phi Delta Theta (2) — 0%
11. Black Key Bulls (2) — 0%
12. Forest Cycling — 0%
13. Sigma Phi Epsilon — 0%
14. Gray Goat — 0%
15. Phi Kappa Sigma — 0%
16. Phi Kappa Psi — 0%
17. Wright Cycling (2) — 0%
18. Delta Chi — 0%
19. Delta Sigma Pi — 0%
20. Delta Upsilon — 0%
21. Collins Buccaneers — 0%
22. Sigma Phi Epsilon (2) — 0%
23. Sigma Alpha Epsilon (2) — 0%
24. Northern Indiana Cycling — 0%
25. Pi Kappa Alpha — 0%
26. Alpha Tau Omega — 0%
27. Phi Sigma Kappa — 0%
28. Phi Kappa Tau — 0%
29. Collins Buccaneers (2) — 0%
WOMEN
1. Teter — 33.33%
2. Alpha Chi Omega — 22.22%
3. Kappa Alpha Theta — 22.22%
4. Cru — 16.67%
5. Alpha Gamma Delta — 0%
6. Delta Gamma — 0%
7. Melanzana — 0%
8. Phi Mu — 0%
9. Wing It — 0%
10. Alpha Xi Delta — 0%
11. Ski — 0%
12. CSF — 0%
13. Kappa Delta — 0%
14. Kappa Kappa Gamma — 0%
15. Theta Phi Alpha — 0%
16. Kappa Delta (2) — 0%
17. Gamma Phi Beta — 0%
18. Collins — 0%
19. Alpha Sigma Alpha — 0%
20. Delta Sigma Pi — 0%
21. Collins (2) — 0%
22. RideOn — 0%
23. Zeta Tau Alpha — 0%
24. Army — 0%
25. Alpha Delta Pi — 0%
26. Delta Zeta — 0%
27. Delta Phi Epsilon — 0%
28. Alpha Epsilon Phi — 0%
29. Alpha Omicron Pi — 0%
30. Air Force — 0%
31. Collins (3) — 0%
32. IU Nursing — 0%
33. Sigma Delta Tau — 0%
34. Mezcla — 0%
35. RideOn (2) — 0%
36. Chi Omega — 0%
As I mentioned in my first post, there’s a lot more to winning the Little 500 than a 33-to-1 chance. So much can go wrong — or right — on race day that predicting the winner with 100 percent accuracy is impossible.
But analyzing the probability of winning based on Team Pursuit and Quals position can yield a weighted average probability of winning the race. It’s not perfect, but if I were a betting man, I’d be putting money on Black Key Bulls and Teter (who had the best average ITT placement and won the white jersey over Alpha Chi Omega) on race day.
MEN
1. Black Key Bulls — 17%
2. Cutters — 12%
3. Phi Delta Theta — 7%
4. Phi Gamma Delta — 4%
5. Beta Theta Pi — 3%
6. Sigma Phi Epsilon — 2%
7. Delta Tau Delta — 2%
8. Sigma Alpha Epsilon — 2%
9. CSF — 1%
10. Phi Kappa Sigma — 1%
11. Evans Scholars — 1%
12. Lambda Chi Alpha — 1%
13. Phi Kappa Psi — 0%
14. Sigma Chi — 0%
15. Sigma Alpha Mu — 0%
16. Sigma Pi — 0%
17. Dodds House — 0%
18. Forest — 0%
19. Wright Cycling — 0%
20. Delta Sigma Pi — 0%
21. Gray Goat — 0%
22. Delta Chi — 0%
23. Pi Kappa Phi — 0%
24. Alpha Epsilon Pi — 0%
25. Alpha Tau Omega — 0%
26. Delta Upsilon — 0%
27. Pi Kappa Alpha — 0%
28. Collins — 0%
29. Northern Indiana Cycling — 0%
20. Phi Kappa Tau — 0%
31. Kappa Delta Rho — 0%
32. Sigma Nu — 0%
33. Phi Sigma Kappa — 0%
WOMEN
1. Teter — 11%
2. Alpha Chi Omega — 11%
3. Cru — 10%
4. Kappa Alpha Theta — 8%
5. Melanzana — 5%
6. Phi Mu — 4%
7. WingIt — 1%
8. Delta Gamma — 1%
9. CSF — 0%
10. Alpha Gamma Delta — 0%
11. Ski — 0%
12. Theta Phi Alpha — 0%
13. Army — 0%
14. Alpha Omicron Pi — 0%
15. Gamma Phi Beta — 0%
16. Kappa Kappa Gamma — 0%
17. Collins — 0%
18. Air Force — 0%
19. Kappa Delta — 0%
20. Chi Omega — 0%
21. Delta Sigma Pi — 0%
22. RideOn — 0%
23. Mezcla — 0%
24. Zeta Tau Alpha — 0%
25. Alpha Xi Delta — 0%
26. Alpha Delta Pi — 0%
27. IU Nursing — 0%
28. Sigma Delta Tau — 0%
29. Alpha Sigma Alpha — 0%
30. Delta Zeta — 0%
31. Delta Delta Delta — 0%
32. Delta Phi Epsilon — 0%
33. Alpha Phi — 0%
Charles Scudder is a senior studying journalism at IU. He put off taking a required statistics course until the last semester of his senior year, so go easy on him if his math is screwy. Email him at cscudder@indiana.edu.
33to1 
The one major flaw with this is if you have one great rider and 1-2 riders that can sit in and be maybe top 40-50 in ITT’s, then they have a much better shot than 0 to win. There are at least 2 women’s teams and maybe 1 men’s team that fit into this category. And quals also throws it off as a couple mistimed quals runs can artificially make a team look bad. Team Pursuit shows depth, miss n out shows strength, and ITT’s shows some good information for the top guys but there tends to be more uneven performances in ITT’s. Also, if you have two awesome guys and a guy having a bad day on Team Pursuit, they will get shelled but have a great chance at winning. I think the white jersey results may be the best indicator for a top 3 performance barring crashes but again it leaves out the possibility of a team with two top riders but poor results in Team Pursuit because the guy couldn’t keep up. I don’t know how that stacks but but at least it’s a combination of 3 events showing different skills rather than 1 or 2.
I’m no math major but I feel like the set of percentages should add up to 100%, otherwise I’m assuming “jesus comes back mid-race and there will be no winner” makes up the remainder
The people really just want in depth knowledgeable power rankings.