# Event Table Primer

### From Dmbwiki

The event tables are what apparently drive most of the outcomes within DMB. Each player has an event table for hitting and for pitching. This can be seen in the dmbplyr file.

The event tables are broken down into play outcomes and pitch outcomes. The investigation into pitch outcomes is still in its infancy so this article will focus mainly on play outcomes (exclusively for hitters).

The play outcome is broken up into 10 categories: singles, doubles, triples, home runs, hit batsmen, base on balls, strikeouts, fly outs, grounded double plays, and ground outs.

The player file (dmbplyr.dat) and the era file (dmbera.dat) both have event table information. The player event table seems to be in relation to the event table in the era file (along with some park factors thrown in on top). The era event table's categories when summed equal 1000 (1000 plate appearances). The era event table seems to be some sort of normalization of the era's statistics (though not a perfect one). The player event table seems to be an adjustment to the era event table.

The neutral era's event table is listed below.

CATEGORY | VALUE |

1B | 164 |

2B | 29 |

3B | 10 |

HR | 27 |

HB | 8 |

BB | 110 |

SO | 128 |

AO | 252 |

GDP | 97 |

GO | 175 |

A switch hitting player with the statistics listed below will get the DMB calculated event table shown below (using neutral era/neutral park and having DMB calculate SF and GDP).

STAT | INPUT | OUTPUT |

AB | 900 | |

H | 232 | |

1B | | 0 |

2B | 36 | 0 |

3B | 7 | 0 |

HR | 20 | 0 |

BB | 94 | 0 |

IBB | 8 | |

HB | 6 | 0 |

SO | 101 | 0 |

AO | | 0 |

GDP | | 1 |

GO | | 0 |

Essentialy with the stats above you get an average player. Some work has been done in trying to determine how DMB calculates the event tables. If one stat is adjusted and all the others remain the same, a good trend can be determined for any given event table category. Some of these trends can be seen below.

Looking at the graphs above, there appears to be a trend among the trends. The slopes of all of the trend lines above fall within the range of 1.2 and 1.3. Also, the y-intercepts appear to be very close to the DMB calculated value for the specific event for two times the average inputted value multiplied by negative one (huh!). If the graph has two equations, the second one has been forced to intercept the y-axis at this two times value.

The trends are clearly linear. However at the extreme fringes (+/- 3 times the average value), the graphs appear to "drift". More investigation into this area is required. It is believed that DMB does some "watering down" with low usage players.

The equations below come very close to calculating the DMB event tables (excluding extreme cases).

**1B_Event:**

1B_Event = ( ( [ ( H*1000 ) / PA ] * 1.2483) -290 ) - (2B_Event + 3B_Event + HR_Event)

**2B_Event:**

2B_Event = ( [ ( 2B*1000 ) / PA ] * 1.2304) - 45

**3B_Event:**

3B_Event = ( [ ( 3B*1000 ) / PA ] * 1.2213) - 8

**HR_Event:**

HR_Event = ( [ ( HR*1000 ) / PA ] * 1.2464) - 25

**HB_Event:**

HB_Event = ( [ ( HB*1000 ) / PA ] * 1.1912) - 7

**BB_Event:**

BB_Event = ( ( [ ( BB*1000 ) / PA ] * 1.2444) - 117 ) + IBB_Component

**SO_Event:**

SO_Event = ( [ ( SO*1000 ) / PA ] * 1.2444) - 125

**AO_Event:**

AO_Event = { ( ( ( [ ( AB*1000 ) / PA ] ) - ( [ ( H*1000 ) / PA ] + [ ( SO*1000 ) / PA ] ) ) * 1.2912) - 728 } * 0.46

**GO_Event:**

GO_Event = { ( ( ( [ ( AB*1000 ) / PA ] ) - ( [ ( H*1000 ) / PA ] + [ ( SO*1000 ) / PA ] ) ) * 1.2912) - 728 } * 0.54

**GDP_Event:**

GDP_Event = ( [ ( GDP*1000 ) / PA ] * 6.1601) - 127

It is believed that the GDP_Event equation is not complete or completely accurate. It is definitely related to the GO_Event (duh!).