The park that a player is playing in frequently factors into how we evaluate them in a DFS slate.  It's common knowledge that hitters have more upside at Coors Field in Colorado and that pitchers are safer plays at Safeco Field in San Diego.  But DraftKings, FanDuel and Yahoo DFS salary setters know this too, and players' salaries frequently reflect the stadium that they're playing in.

Advanced Sports Analytics' ParkFX tables feature three z-score metrics for evaluating players based on the stadium that they are playing at.  For those that aren't familiar with z-scores, a z-score is the number of standard deviations above or below the mean of a particular metric.  So a z-score of 0 is average, a negative z-score is below average, a positive z-score is above average.

  1. Fantasy Points Z-Score ("FD_points", "DK_points", and "YH_points" columns*) - These are the z-scores of the average of the mean fantasy points scored by each player at each park relative to the mean fantasy points scored by each player at all other parks.  These metrics help to identify which ballparks pitchers and hitters are most productive in from a fantasy standpoint under three different DFS scoring schemes.
  2. Fantasy Salary Z-Score ("FD_salary", "DK_salary", and "YH_salary" columns) - Naturally, salary setters have a good feel for which parks pitchers and hitters are most and least productive in, and thus their daily salary is reflective of the stadium that they are playing in.  This metric is the z-score of the average of the mean salary of each player at each park relative to the mean salary of each player at all other parks.  These columns provide information on which parks players' prices are most inflated in under each DFS contest site.
  3. Points-Salary Differential Z-Score ("Diff_FD", "Diff_DK", and "Diff_YH" columns) - These are difference Fantasy Point Z-Scores and Fantasy Salary Z-Scores (Points Z-Score minus Salary Z-Scores).  The interpretation of this metric is quite as straightforward as (1) and (2), but it is possibly the most valuable metric to understand and strategize around.  A Points-Salary Differential Z-Score of zero means that players playing at a certain park are perfectly priced.  That is, if a park yields a Fantasy Points Z-Score of 1 (meaning players' fantasy production on average is 1 standard deviation higher when playing at a park than when playing elsewhere) and they are priced 1 standard deviation higher when playing at a given park (Fantasy Salary Z-Score = 1), players are priced in perfect proportion at the specified park.  However, a positive Points-Salary Differential Z-Score means that the Fantasy Points Z-Score exceeds the Fantasy Salary Z-Score, meaning that for a given park average salary inflation is not as high as the associated fantasy point inflation.  On the flip side, a negative Points-Salary Differential Z-Score means that players are overpriced given the average fantasy point inflation associated with a specified park.

* FD = FanDuel, DK = DraftKings, YH = Yahoo

Each table below can be sorted by any column by clicking on the column.

Hitters

StadiumFD_pointsFD_salaryDK_pointsDK_salaryYH_pointsYH_salaryDiff_FDDiff_DKDiff_YH
CIN1.92-0.401.781.191.920.112.320.591.81
NYM0.94-0.560.95-1.170.96-0.471.502.121.43
TEX1.40-0.101.381.161.37-0.341.500.221.72
MIN0.71-0.260.74-0.200.70-0.660.970.941.35
BAL0.73-0.160.791.520.750.770.89-0.73-0.02
ATL0.980.100.930.550.960.600.880.380.35
CHW1.000.130.910.381.020.610.870.530.41
MIA0.42-0.290.43-0.240.44-0.270.720.680.71
OAK0.910.260.93-0.830.910.120.651.760.78
MIL0.59-0.050.630.790.64-0.390.64-0.161.03
DET0.840.250.88-0.210.790.800.581.09-0.01
PHI0.700.180.720.480.710.270.520.240.44
WAS-0.10-0.61-0.09-1.20-0.10-1.200.511.111.10
CHC0.32-0.040.30-0.630.33-0.350.360.930.68
TOR0.370.070.41-0.770.370.400.301.18-0.03
ARI-0.18-0.43-0.220.11-0.17-0.500.26-0.330.33
SFO-0.36-0.38-0.24-1.15-0.34-0.220.020.91-0.12
KAN-0.13-0.12-0.12-0.24-0.150.46-0.010.12-0.61
BOS-0.64-0.34-0.550.70-0.64-0.52-0.30-1.25-0.12
SDG-0.63-0.26-0.730.07-0.65-0.33-0.38-0.80-0.32
PIT-0.440.09-0.320.12-0.410.52-0.54-0.45-0.93
STL-0.98-0.25-0.94-0.71-0.98-0.77-0.74-0.24-0.20
LAD-1.84-0.97-1.87-1.44-1.84-1.18-0.86-0.44-0.66
SEA-0.810.08-0.84-0.60-0.840.26-0.88-0.24-1.10
TAM-1.25-0.29-1.27-0.94-1.23-0.76-0.96-0.32-0.48
HOU-1.21-0.24-1.29-0.43-1.21-0.92-0.98-0.86-0.29
LAA-0.790.40-0.73-0.11-0.790.72-1.19-0.62-1.51
CLE-2.02-0.76-2.030.57-2.03-0.53-1.26-2.60-1.50
NYY-1.38-0.09-1.48-0.19-1.39-0.53-1.29-1.29-0.86
COL0.925.030.953.430.914.30-4.11-2.48-3.39

Pitchers

StadiumFD_pointsFD_salaryDK_pointsDK_salaryYH_pointsYH_salaryDiff_FDDiff_DKDiff_YH
COL-1.89-4.62-1.90-2.79-1.90-4.252.730.902.35
TOR1.44-0.321.25-0.001.33-0.141.761.251.47
TAM1.240.271.070.550.970.880.970.520.10
SDG2.191.272.151.192.020.870.920.961.14
WAS0.39-0.460.39-1.300.31-0.560.861.690.87
SFO0.950.270.931.730.970.270.68-0.800.70
LAA1.280.681.301.301.38-0.450.60-0.011.83
ATL0.960.370.94-0.111.030.900.591.050.13
CHW0.760.180.750.500.751.440.580.25-0.69
PHI0.680.230.931.470.850.750.45-0.540.10
SEA0.920.660.840.640.860.530.260.210.32
KAN0.20-0.010.270.530.430.070.20-0.270.36
BAL-0.30-0.41-0.30-0.72-0.330.150.110.42-0.48
CHC-0.02-0.08-0.010.13-0.04-0.310.07-0.140.27
NYM0.500.510.740.690.75-0.29-0.010.051.04
STL-0.17-0.12-0.150.01-0.160.01-0.05-0.15-0.17
CIN0.670.770.520.160.530.90-0.090.37-0.37
NYY-0.53-0.31-0.47-0.59-0.48-0.03-0.230.12-0.45
OAK0.070.420.130.640.120.33-0.35-0.52-0.20
CLE-1.21-0.67-1.08-0.92-1.05-0.98-0.54-0.17-0.07
PIT-0.330.26-0.260.76-0.200.04-0.59-1.02-0.24
MIL-0.050.55-0.03-0.52-0.120.22-0.600.49-0.34
LAD-0.300.36-0.18-0.66-0.10-0.01-0.660.48-0.10
HOU-1.32-0.64-1.38-1.25-1.29-1.09-0.68-0.14-0.20
ARI-1.10-0.27-1.19-0.96-1.240.43-0.84-0.23-1.67
BOS-1.35-0.50-1.44-1.29-1.45-0.83-0.85-0.15-0.62
TEX-1.47-0.23-1.50-0.61-1.550.22-1.24-0.89-1.77
DET-0.970.28-1.151.01-1.140.88-1.25-2.16-2.02
MIN-0.800.46-0.86-0.27-0.960.23-1.26-0.59-1.19
MIA-0.441.09-0.300.68-0.29-0.18-1.53-0.98-0.11