Home Field Advantage
Sometimes I’m not sure which is worse, that my system has been losing routinely this season or that I continue to spend my Saturdays actually watching. That’s a lot of wasted time on what’s clearly the best day of the week. I’ll keep doing it too, but regardless of whether or not my Saturdays become more entertaining to me, and I think they will, I’d like to take a minute to reflect back on the Texas A&M upset of Florida I watched this past weekend. In typical 2020 fashion, I was on the wrong end of this, but it sure felt great for awhile, as the Gators moved the ball with ease and it was clear to me that the underdog wouldn’t be able to keep up. As the Aggies did, in fact, demonstrate that they would be keeping up, it was also evident that the huge crowd they had in College Station noticed it too, and as they got really loud, I remember thinking, well that’s not very 2020. Argue amongst yourselves about whether this large crowd situation is a great thing or actually a terrible thing, and since 2020 also features plenty of arguments, it seems likely that you might, but what I have been thinking about for the last few days is home field advantage and how it might be messing with my numbers.
About the time of the last World Cup in 2018, I listened to a Freakonomics podcast about the upcoming soccer event, which featured a variety of relevant subjects at the time: the brilliance of Lionel Messi, corruption in FIFA, and also somewhere in the middle, an economics professor from Yale talking about home field advantage in sports. It’s fascinating and you can listen to it here if you’d like. First, the professor, Toby Moskowitz is his name, acknowledges what we already know, that home field advantage in sports is a very real thing, influencing some sports more so than others, but is relevant to all. And then he goes into the why and this part I remember captivating me quite literally, as I risked being late, stuck sitting in my car in the parking lot of my destination just to hear this out. It seems that of all our theories, travel distance, home team adrenaline, home teams built to compete in their particular home stadiums, none move the data quite like the psychological influence of a referee’s natural human subconscious desire to please a crowd. Yep, the zebras. I’ll circle back.
If you look up home field advantage in college football, the number you will most certainly get is an average of 3.8 points. I’m sure it will come as no surprise to you that I’ve thought a lot about this number. Mostly, the thing I’ve considered is that I have no idea what parameters are used in the development of this average. How many years does it date back? I mean, the game changes over time, as all games do, not only with new strategies and rule changes, but also the level of talent and conditioning of the athletes participating. Also, are all home fields to be treated equally? Air Force plays in the mountains. Hawaii plays on an island. Is the Big House in Michigan a similar experience to playing a road game at San Jose State? It seems silly to suggest that it might be. I’ve toyed with the idea of ascertaining unique home field advantage quantities for each home team, and might resume the notion in the future, but so far the problem I keep coming up with is, once again, the sample size of a football season. With only, say, 7 home games in a season, one would need to put together several seasons to come up with a reliable average, and by that time a lot can change regarding crowd size. If you can’t think of a reference point for this, you can use mine: think of the packed house that the Mangino/Reesing Kansas 2007 team played in front of, and then the empty student sections of the post-halftime Turner Gill games 3 years later. So far then, I’ve stuck with using the 3.8 number in my methods, and until now it seems to have been a useful number, particularly when it comes to backing off of a game that I might otherwise be playing the road team. Scrutiny though, in a deal like this, almost always trends more inevitable than it is merely possible, as it should, and I have a feeling I’m not finished with overthinking the concept.
The problem of 2020 is the inconsistency in a project that I exclusively find value in consistencies. It’s not just the inability to predict when home field advantage is more of a thing than others in a year like this, but also the butterfly effect of a result differing from one game’s home field expectation and then influencing the prediction of another, which might have an entirely different crowd element in play. Think back to the referees again. If they are really all that influenced by the tendency to not want to disappoint the masses, would they really be all that influenced by a stadium that is merely pumping noise into the stadium through large speakers? If it is truly a subconscious thing, then surely the subconscious is also telling them that there are not actually people making this noise. And in football, even with instant replay, referees can still make a huge difference in a game just on pass interference calls and no calls alone. So how do I measure that? Well, I can’t with the resources I have available, as well as the amount of time I have to apply to it. So all I can do is let the data play out, and allow my system to make its adjustments, and hopefully the results will start to find a balance sooner than later, as well as some consistency to work with.
How bad has it been? Very, to be honest. I’m 11-21 now, with a system that has not finished below 58% since I came up with some key realizations that appear to be off track this season in many ways, as home field advantage missteps are likely only a few of many factors. I do take some solace in that a regression to the mean is probably inevitable, but then again, I’m also aware that this is the very definition of gambler’s fallacy. After all, while I may have what feels like a large enough sample to set my mean at about 59%, if I go down that road I would have to consider that in a much larger sample, I might be no better than the 50% coin toss method, and that what I’m experiencing now is a massive regression to that mean, in the wrong direction. I’m optimistic enough that I’d rather avoid that consideration, so for the most part I abide the gambler’s fallacy warning and allow the future to remain independent of the past. Additionally though is the hopelessness of inactivity. While it’s true that I’ve had 14-2 or 13-3 weeks in the past, and one of those would bring me back to even in the timespan of one very joyous Saturday, I also have a noticeable absence of 16 game weeks to play and this one upcoming is no different. But all that being said, I still believe in the system and since I’m a guy that finds value in the journey, I carry on, and hopefully you will too along with me.
The system’s picks this week:
Thursday night
Georgia State +3 on the road vs Arkansas State
Saturday
Tulsa +3.5 at home vs Cincinnati
North Carolina State -4.5 at home vs Duke
Middle Tennessee -6.5 at home vs North Texas
Georgia +4 on the road vs Alabama
UTEP +6.5 at home vs Southern Mississippi
Just 6 games again. Ugh. Thankfully, the end is near regarding the vulnerability of low sample size, as the dormant conferences draw closer to returning. I do have a couple games for Saturday that I am still tracking the spread movement for, but nothing close enough to warrant mention at this time. Thanks again for reading.
