Home > 2012 Season, 2012/2013 Season, College Football, General Information, ncaaf, ncaaf trends, NFL, Trends, Twitter Question, User Questions > Football Is At The Quarter Mile Post And History Suggests That Some Teams Perform Measurably Different In The Next Leg Of Their Seasons

Football Is At The Quarter Mile Post And History Suggests That Some Teams Perform Measurably Different In The Next Leg Of Their Seasons

If you’re like us, whenever football is not being played you can’t wait for it to start, then once it does the weeks just fly by.

It’s hard to believe that we are already one quarter of the way into the NFL season, and one quarter plus into the College Football season.

One of the things we have tracked for some time but never reported on, is an analysis of how teams have done comparatively across different time frames with in a season.

For football, we have found that by taking the regular season and grouping it into quarters we have gotten that best results overtime when it comes to this type of comparative analysis.

The time-frames are defined as follows:

  • For both the NFL and NCAAF – Weeks 01-04 are Q1
  • For both the NFL and NCAAF – Weeks 05-08 are Q2
  • For both the NFL and NCAAF – Weeks 09-12 are Q3
  • Q4 for the is NFL Weeks 13-17, and for NCAAF Weeks 13-Through the Bowl Games

Everyone reading this is probably well aware that at the end of any football season, there is a fairly even distribution of Favorites and Dogs that cover ATS.  But you will never see an even distribution of Favorites and Dogs from week to week.  A few weeks might be top heavy with Favorites followed by a run of Dogs, that at the end of the season results in relative ATS parity.

The same basic philosophy can be applied when analyzing result data at the individual team level across various time-frames.

For example, the Florida Gators historically have been overall a very strong 60.6% ATS in the past 5 years.  But when we review how the Gators have done across our time-frames we see an interesting and consistent trend.  For Q1 the Gators have been an off the charts 76.2% ATS, but for Q2 their ATS win rate drops to only 33.3%.  Currently the Gators are 3-1, or 75.0%, ATS and host LSU as a 2.5 point Dog in this Q2 matchup.

In another example, the Minnesota Vikings have gone Over the Total 52.4% of the time for the past five years of history.  But for Q1 only across that five year history the Vikings Total has gone Over only 39.1% of the time, while for Q2 they have been Over 72.2% of the time.  This week Minny, who is 1 Over/3 Under on the year, hosts the Titans with the Total currently at 44.

We want to be absolutely clear, that any of these trends in a vacuum are no reason in and of itself to play a side or total.  This data should be used as just one more piece of information in your handicapping.

That said, there has been a noteworthy consistency to this data for several teams, and we believe it is very much worth your while to familiarize yourself with the information.

There are a lot of arguments that can be made as to why these trends occur for some teams from quarter to quarter over time.  Coaching philosophies, scheduling, QB’s that start slow, and odds-makers’ adjustments all play apart in the fluctuations we see between time-frames for some of the teams.

When using this reference it is important that you keep in mind situations such as coaching or impact roster changes that would render historic data much less useful.  Obviously for teams that have enjoyed coaching and player continuity, the data should tend to hold more true.

The screenshots and spreadsheet show historic Q1/Q2 and baseline data for the NFL and College Football.  The baseline represents all results for a given team across all time-frames. We will revisit this post at the end of Q2 and see how the data fared versus the actual results, as well as add data for Q3.

In the embedded Excel workbook below, you will find the following worksheets for both the NFL and NCAAF:

  • ML_HISTORY_Q1-Q2 – ML WIN % BY TEAM FOR BASELINE/Q2/Q1 – SORTED BY ALPHA AND BY Q2 WIN % AND BY Q2 DIFFERENCE FROM BASELINE AND BY Q2 DIFFERENCE FROM Q1
  • ATS_HISTORY_Q1-Q2 – ATS WIN % BY TEAM FOR BASELINE/Q2/Q1 – SORTED BY ALPHA AND BY Q2 WIN % AND BY Q2 DIFFERENCE FROM BASELINE AND BY Q2 DIFFERENCE FROM Q1
  • TOTAL_HISTORY_Q1-Q2 – TOTAL OVER WIN % BY TEAM FOR BASELINE/Q2/Q1 – SORTED BY ALPHA AND BY Q2 WIN % AND BY Q2 DIFFERENCE FROM BASELINE AND BY Q2 DIFFERENCE FROM Q1
  • SUMMARY_HISTORY_Q1-Q2 – ML/ATS/TOTAL COUNTS BY FAV/OVER FOR Q1, Q2, AND A BASELINE PERIODA

Please note the following regarding the data:

  • THE DATA REPRESENTS A 5 YEAR HISTORY AND IS GROUPED BY QUARTER
  • ML AND ATS PERCENTS ARE IN TERMS OR WIN %
  • TOTAL PERCENTS ARE IN TERMS OF PERCENT OF GAMES THAT WENT OVER
  • THE BASELINE PERIOD REPRESENTS ALL DATA FOR ALL TIME-FRAMES BY TEAM
  • ALL PUSHES HAVE BEEN OMITTED

NFL DATA

COLLEGE FOOTBALL DATA

We hope you find the information an asset to your handicapping and please visit our free site, GreyMatterStats, where we put this information at your fingertips.

If you find something of interest that you would like to share, please feel free to leave a comment or Tweet us  @GreyMatterStats

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