# The Micro-Level View Of Downs And Success: Yet Another View

DOWNS AND SUCCESS: THE MICRO-LEVEL VIEW

*NOTE: The scales on charts will vary - apologies in advance.*

Inspired partially by a suggestion made in the last Dear Diary, I’ve decided to extend this series one more entry. This time, I am going with the micro-level view, using in-game differentials and tracking these with point differentials from game to game. The sample I chose, just to see if there is anything going on within the numbers, is Michigan’s in-game stats from 2005-2012.

The method for calculating that differential was the same as in the previous entry – the offensive conversion rate minus the defensive (opponent’s success) rate. The point differential would then be simply Michigan’s total points minus the opponent. The idea was to see if they tracked together and, if so, how close is the relationship.

As it turns out, it is reasonably close – for the sample, the R-value came out to be 0.728, so there is a decided correlation between the two variables. It was at this point that I decided to embark on a smaller side comparison of first down differential (Michigan’s total 1^{st}downs minus opponent first downs) and also compare these to point differentials. I only did this for three seasons, but for that small sample, the R-value was 0.653, so although it is a weaker relationship, there is still a relationship here.

A LITTLE SUMMARY DATA:

First, here are the average differentials for each season studied and the average point differential:

YEAR |
AVG. 3RD DOWN DIFFERENTIAL |
AVG. POINT DIFFERENTIAL |

2005 |
5.53% |
8.42 |

2006 |
9.89% |
13.31 |

2007 |
8.38% |
5.85 |

2008 |
-11.43% |
-8.67 |

2009 |
3.45% |
2.00 |

2010 |
0.28% |
-2.46 |

2011 |
10.43% |
15.92 |

2012 |
16.90% |
10.00 |

For the entire sample of 101 games, the overall average differential for Michigan was 5.62% and the average point differential was 5.69. The standard deviations were 22.45% and 20.16 points respectively. Interestingly, the median value is 2.82% for the 3^{rd}down differential and the median point differential is actually 4.00.

Second, here is a table which shows how many games in each of the eight seasons had a negative differential versus how many had a negative point differential. Actually, I left that in the table header because it is perhaps an excellent jargon term for a loss.

“We didn’t lose, but merely achieved a negative point differential.”

If only I had thought about this in 2008. Anyway, here’s the table:

SEASON |
GAMES WITH NEGATIVE 3rd DOWN DIFF. |
GAMES WITH NEGATIVE POINT DIFF. (LOSSES) |

2005 |
4 |
5 |

2006 |
4 |
2 |

2007 |
5 |
4 |

2008 |
10 |
9 |

2009 |
6 |
7 |

2010 |
7 |
6 |

2011 |
5 |
2 |

2012 |
3 |
5 |

The 2011 and 2012 numbers stood out to me, and in the last diary, someone pointed out the strange decoupling of differential and win percentage in this period for Michigan actually. In these tables, you can see just what that looked like. The average 3^{rd}down differential went up from the previous year, but the average point differential fell. We only lost the battle of 3^{rd}downs three times but lost five games. Indeed, in the 2011 season, we lost the battle of 3^{rd}downs five times but only lost two games.

THIRD DOWN DIFFERENTIAL / POINT DIFFERENTIAL CHARTS:

FOR GIGGLES - FIRST DOWN DIFFERENTIAL / POINT DIFFERENTIAL (only for 2010-2012):

TL;DR CONCLUSION:

Essentially, this is testing the usefulness / limitations of this particular metric as a predictive tool, but doing so at the level of the games in the season and not just season averages. I like to think that it is reasonably useful if not always accurate.

OBLIGATORY:

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