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).
Sports Betting Charts
Wednesday, December 25, 2013
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:
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:
Sources:
Kubatko, Justin (November 13, 2013). "Home Win Probability Using SRS." Statitudes. Retrieved November 26, 2013.
- 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
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:
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
Subscribe to:
Posts (Atom)