Wednesday, December 25, 2013

Soccer - Converting 1x2 Moneyline Into Asian Handicap

Variables
P(Aw) = Probability of team A winning
P(d) = Probability of draw
P(Bw) = Probability of team B winning
P(A|1) = Probability of Team A winning by exactly 1 goal = 0.191856-0.30565416*x+1.42165*x^2-1.21885522*x^3, where x=P(Aw)
P(A|2) = Probability of Team A winning by exactly 2 goals = 0.13482-0.553848*x+1.71645*x^2-1.08417508*x^3, where x=P(Aw)
TO(A|X) = True decimal odds of team A with handicap X

True Odds, A[1]










Notes and Sources
[1] To find true odds of Team B, use the following equation: 1-TO(A|X).

Tuesday, November 26, 2013

NBA - Home Win Probability Using SRS

The following equations can be used to determine true money-line and true point spreads for the NBA. The basis of them are using Adjusted Simple Rating System (aSRS), which can be calculated by:

 

      Where:
  • aSRS = adjusted simple rating system
  • G = games played
  • SRS = simple rating system

Simple Rating System—from what I've seen—is only published on Basketball-Reference, and is calculated by adding the team's margin of victory and strength of schedule. Justin Kubatko of Statitudes compiled the results of the 1976–1977 through 2012–2013 NBA seasons using aSRS, and created the following logit equation to predict the probability of the home team winning:


      Where:
  • e = Euler's number, the base of natural logarithm
  • dSRS = Home team's aSRS – Away team's aSRS
This answer will produce a decimal (i.e. 0.5277) which can be multiplied by 100 to get the implied probability (i.e. 52.77%). This implied probability can be converted into a true money-line by using a convertor. Finally, this probability can be converted into a point spread using the following equation:


Sources:
Kubatko, Justin (November 13, 2013). "Home Win Probability Using SRS." Statitudes. Retrieved November 26, 2013.

NFL Playoff Predictions - Week 12

Methodology:
At NFL-Forecast, there is a program that uses a monte carlo simulator to simulate the remainder of the season 5,000 times. It also takes the team efficiency ratings from Advanced NFL Stats.

Division Standings:

AFC East
New England 96.18%
NY Jets 1.32%
Miami 1.30%
Buffalo 1.20%


AFC North
Cincinnati 95.40%
Pittsburgh 3.50%
Baltimore 1.08%
Cleveland 0.02%


AFC South
Indianapolis 96.90%
Tennessee 3.10%
Houston 0.00%
Jacksonville 0.00%


AFC West
Denver 89.88%
Kansas City 10.08%
San Diego 0.04%
Oakland 0.00%


NFC East
Philadelphia 65.86%
Dallas 34.04%
NY Giants 0.10%
Washington 0.00%


NFC North
Detroit 51.22%
Green Bay 32.64%
Chicago 16.14%
Minnesota 0.00%


NFC South
New Orleans 90.04%
Carolina 9.96%
Tampa Bay 0.00%
Atlanta 0.00%


NFC West
Seattke 98.74%
San Francisco 1.06%
Arizona 0.20%
St. Louis 0.00%

Tuesday, October 15, 2013

CBB - Middling Totals


Difference Middle EV @ -110
0 1.24% -$7.39
0.5 3.25% -$3.17
1 5.87% $2.32
1.5 7.74% $6.25
2 10.23% $11.48
2.5 12.21% $15.64
3 14.72% $20.91
3.5 16.69% $25.04
4 19.42% $30.78

Source: Detroit, Johnny (January 18, 2012). "Middling Totals." Pregame.com. Retrieved October 11, 2013.

CBB - Middling Sides

Difference Middle
0.0 2.04%
0.5 5.71%
1.0 9.60%
1.5 13.20%
2.0 17.23%
2.5 20.99%
3.0 24.90%
3.5 28.85%
4.0 32.51%

Source: Detroit, Johnny (January 18, 2012). "Middling Totals." Pregame.com. Retrieved October 15, 2013.

MLB - Various Proposition Equations

Batter's True Batting Average Against Certain Pitcher:[1]




     Where:
          BA = Player's Batting Average
          BAA = Pitcher's Batting Average Against
          LBA = League's Batting Average

Total Number of Hits in a Game by Player X:




     Where:
           TBA = True Batting Average
           AB/G = At Bats Per Game

Park Factor:




     Where:
           OBP = On Base Percentage
           OBPA = On Base Percentage Against
           N = Number of Games

Probability of Team Scoring Run in First Inning:[2]

     Where:
           A = Team's Runs/Game

Sources:
[1] Sugano, Adam (2008). A Player-Based Approach to Baseball Simulation (Dissertation). University of California, Los Angeles. p. 50.
[2] Woolner, Keith (n.d.) An Analytic Model for Per-Inning Scoring Distributions (Report.) Baseball Prospectus. pp. 8–10.

Monday, October 14, 2013

Pythagorean Theorem for Season Wins


x = 1.83 - MLB, 2.37 - NFL, 16.50 - NBA, 10.00 - CBB, 2.00 - NHL, 1.70 - MLS