Month: March 2016

My 2016 NCAA Bracket

~ Kevin Indrebo ~ Leave a comment

In my previous post, I introduced the quantitative rating system I’m using this year as a guide for filling out my bracket. I didn’t go strictly by the book – I went with the lower rated team on a small number of occasions. I did this mostly because the system’s picks were too boring (not enough upsets). Without further ado…

bracket2016

My first discretionary call was taking Yale over Baylor. I couldn’t NOT pick a 12-5 upset, and according to my system, this is the most probable one.

I then took Cincinnati over Oregon in the second round, even though Oregon is rated higher. It seems like this year has the potential to be really bloody, and I doubt all the 1 seeds will make it through to the next weekend. I have Oregon as the lowest rated 1 seed (by a fair margin), so I knocked those ducks out.

I also felt a need to have at least one low rated seed (10+) in the sweet 16 (I expect there will be more than 1, possibly several). Gonzaga looked like the most probable team to pull this off.

Also, though the ratings on my previous post show Kansas as the #1 team, I reran the algorithm after eliminating the Michigan St games that Denzel Valentine missed due to injury, and that change put the Spartans on top. Was I cheating to get the team I wanted to pick as the champs into the #1 slot in my system? Yeah, sure, maybe. But Valentine is arguably the best player in the nation, and missing him obviously hurt their rating.

One final note before I display the game-by-game win probabilities throughout the tournament (adhering to my system’s calls): I did another trial with my algorithm where I replaced the margin-of-victory game probability extrapolation with one that ignores the score. I noticed a couple of teams rated much higher in those results, and I think they could be dangerous – St. Joe’s and Seton Hall.

Below, you can see the game-by-game probabilities by round given by my system:

First Round

Kansas 93.21% Austin Peay 6.79%
Connecticut 56.33% Colorado 43.67%
Maryland 65.78% San Diego St 34.22%
California 63.98% Hawaii 36.02%
Arizona 53.62% Wichita St 46.38%
Miami FL 80.45% Buffalo 19.55%
Iowa 67.52% Temple 32.48%
Villanova 87.00% UNC Asheville 13.00%
Oregon 92.62% Holy Cross 7.38%
Cincinnati 54.43% St Joseph’s PA 45.57%
Baylor 63.76% Yale 36.24%
Duke 74.28% UNC Wilmington 25.72%
Texas 68.31% Northern Iowa 31.69%
Texas A&M 80.12% WI Green Bay 19.88%
VA Commonwealth 57.44% Oregon St 42.56%
Oklahoma 81.15% CS Bakersfield 18.85%
North Carolina 91.22% FL Gulf Coast 8.78%
USC 57.14% Providence 42.86%
Indiana 76.02% Chattanooga 23.98%
Kentucky 75.99% Stony Brook 24.01%
Notre Dame 53.04% Michigan 46.96%
West Virginia 74.10% SF Austin 25.90%
Pittsburgh 54.58% Wisconsin 45.42%
Xavier 81.93% Weber St 18.07%
Virginia 91.86% Hampton 8.14%
Butler 54.25% Texas Tech 45.75%
Purdue 73.95% Ark Little Rock 26.05%
Iowa St 74.52% Iona 25.48%
Gonzaga 51.98% Seton Hall 48.02%
Utah 76.29% Fresno St 23.71%
Syracuse 53.28% Dayton 46.72%
Michigan St 88.23% MTSU 11.77%

Second Round

Kansas 69.73% Connecticut 30.27%
Maryland 51.87% California 48.13%
Arizona 50.69% Miami FL 49.31%
Villanova 66.34% Iowa 33.66%
Oregon 59.89% Cincinnati 40.11%
Duke 53.83% Baylor 46.17%
Texas A&M 57.86% Texas 42.14%
Oklahoma 65.14% VA Commonwealth 34.86%
North Carolina 70.49% USC 29.51%
Kentucky 52.76% Indiana 47.24%
West Virginia 68.30% Notre Dame 31.70%
Xavier 59.40% Pittsburgh 40.60%
Virginia 66.83% Butler 33.17%
Purdue 55.01% Iowa St 44.99%
Utah 53.54% Gonzaga 46.46%
Michigan St 75.56% Syracuse 24.44%

Sweet Sixteen

Kansas 65.72% Maryland 34.28%
Villanova 59.12% Arizona 40.88%
Duke 51.58% Oregon 48.42%
Oklahoma 57.97% Texas A&M 42.03%
North Carolina 58.84% Kentucky 41.16%
West Virginia 59.09% Xavier 40.91%
Virginia 54.40% Purdue 45.60%
Michigan St 68.07% Utah 31.93%

Elite Eight

Kansas 53.32% Villanova 46.68%
Oklahoma 54.64% Duke 45.36%
North Carolina 52.13% West Virginia 47.87%
Michigan St 56.94% Virginia 43.06%

Final Four

Kansas 57.20% Oklahoma 42.80%
Michigan St 53.62% North Carolina 46.38%

Championship

Michigan St 51.71% Kansas 48.29%

 

Yet Another CBB Rating System

~ Kevin Indrebo ~ Leave a comment

It’s here! March Madness, that time of year where we all try to predict how a bunch of 20-year-olds playing a game filled with randomness will perform. It’s fun! But we sure don’t bet real money on it, cause that would be illegal!

Actually guys, it’s not as much fun for me again this year, because, for the 3rd consecutive season, my team was not invited to keep playing. Someone please fix this. Sigh…

Anyway, this year I decided to build my own quantitative rating and prediction system to help me fill out my bracket. Here’s how my system works:

I define a numeric rating, R(i), for each team i. I also define the probability of team i defeating team j on a neutral court as

P(i->j) = R(i) / (R(i) + R(j)).

I then create a cost function, defined over all games played during the season, which is the sum over

[P(i->j) – GP(i->j)]^2,

where GP(i->j) is an estimated game probability that depends only on the result of each individual game. The GP probability value for each game must be provided as an input to the algorithm.

Of course, coming up with a good value for GP(i->j) is tricky, since we only have one occurrence (one result) for each game. The simplest method for determining GP(i->j) is to assign 100% probability if team i defeated team j, and 0% otherwise. But this ignores home/away, so it’s not ideal.

For my system, I decided to build a model based on the home/away adjusted margin of victory for the game. If team i wins by a large margin, P(i->j) will be close to 100%, but if it’s a close game, the value will be closer to 50%. (The home/away factor adjusts the margin in favor of the road team by 3.5 points)

To compute the final ratings for all teams, I initialize all teams with an equal rating (1.0) and perform an iterative optimization that minimizes the overall cost function with respect to the ratings. I use gradient descent as the optimization procedure.

Below, I list my ratings for the top 100 teams in division 1. (My Marquette Golden Eagles just managed to sneak in at #99, woo!). Note that the value of a team’s rating carries no particular meaning by itself – it’s only useful when compared to the other team ratings.

Rank Team Rating
1 Kansas 8.779
2 Michigan St 8.317
3 North Carolina 8.174
4 Villanova 7.717
5 West Virginia 7.504
6 Virginia 7.149
7 Louisville 6.637
8 Oklahoma 6.602
9 Purdue 6.035
10 Kentucky 5.737
11 Duke 5.483
12 Arizona 5.342
13 Xavier 5.200
14 Miami FL 5.199
15 Oregon 5.151
16 Indiana 5.136
17 Iowa St 4.919
18 Texas A&M 4.802
19 Baylor 4.700
20 Maryland 4.601
21 SMU 4.514
22 Utah 4.400
23 Iowa 4.390
24 California 4.266
25 Vanderbilt 4.246
26 Wichita St 3.983
27 Gonzaga 3.842
28 Connecticut 3.827
29 Pittsburgh 3.552
30 Butler 3.543
31 Seton Hall 3.538
32 VA Commonwealth 3.531
33 Notre Dame 3.496
34 Texas 3.487
35 USC 3.465
36 Cincinnati 3.444
37 Florida 3.443
38 St Mary’s CA 3.221
39 Creighton 3.182
40 South Carolina 3.154
41 Kansas St 3.117
42 Michigan 3.107
43 Syracuse 3.057
44 Texas Tech 2.990
45 Colorado 2.969
46 Wisconsin 2.953
47 St Joseph’s PA 2.882
48 Washington 2.825
49 Florida St 2.791
50 Valparaiso 2.700
51 Dayton 2.682
52 Yale 2.671
53 SF Austin 2.620
54 Oregon St 2.617
55 Georgia Tech 2.589
56 Clemson 2.589
57 Providence 2.559
58 Northwestern 2.529
59 Ohio St 2.515
60 Georgia 2.467
61 Hawaii 2.400
62 San Diego St 2.388
63 BYU 2.353
64 Arkansas 2.330
65 UCLA 2.309
66 Tulsa 2.297
67 G Washington 2.284
68 Virginia Tech 2.209
69 Arizona St 2.182
70 Georgetown 2.167
71 Houston 2.114
72 Ark Little Rock 2.110
73 Rhode Island 2.089
74 Nebraska 2.072
75 UC Irvine 2.046
76 Mississippi 2.045
77 Stanford 2.029
78 LSU 2.023
79 Princeton 2.012
80 NC State 2.006
81 Memphis 2.003
82 Monmouth NJ 1.994
83 Evansville 1.949
84 Alabama 1.925
85 St Bonaventure 1.921
86 UNC Wilmington 1.896
87 Richmond 1.896
88 S Dakota St 1.887
89 Temple 1.884
90 Mississippi St 1.881
91 Stony Brook 1.806
92 Oklahoma St 1.794
93 Akron 1.787
94 Tennessee 1.764
95 Davidson 1.751
96 William & Mary 1.749
97 Santa Barbara 1.732
98 James Madison 1.731
99 Marquette 1.727
100 Iona 1.674

Just the Beginning

~ Kevin Indrebo ~ Leave a comment

After just over 5 months and about 115,000 words, the first draft of Daughters of Darkness is complete!

I found the experience of writing this draft different from my previous manuscripts. Most of the time, I’ll struggle to keep a good pace going early on, but as I near the end, I’ll finish strong, usually averaging a lot more words per day over the last 10-20% of the book than the first 80-90%. In this case, though, I pushed forward faster than normal through the first 70% of the book, but slowed down quite a bit after that.

The main reason is that as I neared the end of Act II, I got a bad feeling about a couple of the plot threads, and I stressed out for awhile, unsure what to do about it. As I talked about in an earlier post, I did far more outlining in this project than I’ve ever done before, and I thought I had all the subplots mapped out nicely. But when I got to a particular part in the story, I started to think what I had planned was going to come off as contrived, messy, and wrong for the characters.

In the end, I decided to cut out a pair of subplots. Or maybe I should say I cut them short. Because of this, the draft ended up shorter than I expected – I had a target of 125,000 words, and I usually overshoot. At the time, I was worried about the decision I was making, and I think that slowed down my progress going forward. As I look back on it now, though, I’m pretty sure I did the right thing.

But now the real work begins.

I’ve always enjoyed writing more than revising. For me, writing is something like 75% play and 25% work, whereas revising is more like the reverse, 25/75. Still, I figure I’ll (mostly) enjoy working to make this MS super awesome.

I thoroughly enjoyed writing this book, though somewhat less after I reached that sticking point. Not every character came out as I expected, but I think some of them ended up better than I’d originally hoped. I don’t know what will come of this project (statistically speaking, almost certainly nothing), but it’s still been a cool experience.