Sunday, July 26, 2015

Projected NCAAF Strength of Schedules

Now that I've calculated my new preseason NCAAF rankings, I can project some things about the upcoming season. I projected each team's strength of schedule (SOS) based on each opponent they'll face this year. FCS teams are taken into account, and are designated a uniform ranking of 0.100 (so the SOS of teams like North Carolina who play 2 FCS teams fall quite a bit). Charlotte is not included in the following rankings, since their page is not yet live on College Football-Reference (where I pull my data from). As you'll see, the top is littered with Big 10, SEC, and Pac-12 teams:

Friday, July 24, 2015

Preseason NCAAF Rankings for 2015

As I did last year, I've created a new set of preseason NCAAF rankings that take into account player turnover and recruiting classes. As before, I used my final Composite ratings from the MDS Model (from last season) as the base (which takes into account both a forward-looking predictive component and a past-performance only retrodictive component), and then factored in ESPN's Preseason FPI and the S&P+ projections, both of which take into account player changes on each team. Once the season starts, this "preseason" rating will be faded out as the season progresses, carrying less and less weight with each ensuing week.

Sunday, July 5, 2015

Home-Field Advantage and Standard Deviations in Scores for All Leagues

One of the main goals of the MDS Model is to predict spreads, i.e. the expected margin of victory in each game. This then predicts games against the spread after Log5 is calculated to determine the win probability for any given game. As I've written before"Log5 only gives the probability of Team A beating Team B (and inversely, Team B beating Team A). A major use of modeling sports is for picking games "against the spread", which is considerably harder than picking "straight up" winners: lines (i.e. spreads) are designed to be 50/50."

To predict margin of victory (MOV), I calculate the inverse of the normal distribution for each percentage and multiply this by a parameter for each league. What follows is a guidebook for the standard deviation in each league, both for the final margin in each game (difference in score) as well as for the totals in each game (sum of both scores):

SD for MOV:
NBA:      13.47
NCAAB:    13.95
WNBA:     12.96
NFL:      9.82
NCAAF:    13.00
NHL:      2.31
MLB:      4.26
MLS:      1.50

SD for Totals:
NBA:      19.13
NCAAB:    19.13
WNBA:     18.68
NFL:      13.48
NCAAF:    18.55
NHL:      2.18
MLB:      4.24
Soccer:   1.52

Additionally, I also calculated the average home-field advantage in each league. In all cases, data was used for either the current ongoing season or the most recent completed season.

NBA:      2.41
NCAAB:    5.39
WNBA:     2.10
NFL:      2.57
NCAAF:    4.39
NHL:      0.24
MLB:      0.19
MLS:      0.55


Wednesday, July 1, 2015

The Rays Keep Getting No-Hit

As a Rays fan, this hasn't been fun to watch, even with the team still only a half game out of first. Even though tonight the no-hitter was broken up with one strike to go, it feels like this has occurred more often to them than any other team in MLB in the past few years. Turns out I was right - here are the number of no-hitters thrown against each team in the past 10 years: