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Comparing Home Court Advantage
Is there any possibility to measure the Home Court Advantage in Lines for each sport.
I figured out that Home Court Advantage is overvalued vs the line in the NBA.
If a team is expected to win ATS in the NBA and is a Home Team they will loose 56% of the time in my metrics.
How do you calculate Home Court Advantage?
It can vary drastically from year to year. Teams change. Player personnel constantly changes. Schedules change. There are too many intangibles to make any rules.
Nash it can also depend on what you consider a home field advantage.
BigDaddy couldn't be more wrong, it actually does not change drastically year from year and the public perception even less so.
If BigDaddy had actually calculated any of them, and actually bet, he might be able to tell you this.
But instead of any of that, he talked out of his as again and his getting run out of the Think Tank, again.
Nash, I can help you, but how are you defining home field advantage? Are you just interested in scores alone? Could there be others?
A score calculation should be obvious, just comparing home and road, but including the opponents might smooth out the curve or even show why the line appears advantageous.
Remember, some teams have a home disadvantage, or even a road advantage.
Overall, it may not be the advantage against the number you had hoped, but some teams might still surprise.
i am very interested in the subject BTW........... and i think home court advantage can vary within a league. and not necessarily in the obvious way (i.e. it's really tough playing at alabama/etc.)
I measure home field advantage is underlying, for example in football,
What is the difference in pass completion, sacks, stuffed runs ect.
This then rolls up to a full line home field advantage later
You need to adjust the home field advantage to the total, ie 3 points on a total of 54 is not the same on a total of 38
https://killersports.com/nba/query?s...+S+D+Q+L+%21++
looks like HFA is 2.5 to 3 points......... it is somewhat situational.
i didn't post this but seems like NBA has a had bias towards favorites being winners......... home or away.
https://killersports.com/nfl/query?s...+S+D+Q+L+%21++
nfl... home has been mixed... 2 to 2.5 points HFA
i guess the big thing with any home field is crazy fans and unfamiliar/tiring surroundings for road players.
but one thing i've thought made sense is that bad, going-nowhere teams at least put up an effort for home fans. otherwise, they'll hear about it. road they don't care.... numbers do NOT bear this out i.e. i'm probably wrong.
Kenpom has home court advantage by team for all Division I NCAA basketball teams. His definition,
Model inputs
(based on last 60 home and road conf. games)
Values are per game differences
between home and road margin
Issue is not players doing better. Issue is refs being biased.Originally Posted by gojetsgomoxies
If you are the home plate umpire on a 3 2 count, and a pitch is marginal. A ball means a stadium of people cheering you. A strike means a stadium of people booing you. Unconsciously, you are going to lean more towards that ball.
Same is true for pass interference and late fouls.
In football the stadium noise covers the audibles, leading to more confusion and higher sacks and lower completions.
The guys who play at the highest level are motivated no matter what. They also get bonuses based on production, they arent going to dog it because they are the away team.
The main factor here for me: Is the public overvaluing Home Court Advantage in certain sports based on numeric criteria.
A simple formula based on offense at home vs defense at home compared to opponents offense away and opponents defense away is overvalued by the public for the home team.
If the expected winner is the home team than an edge of 1621-1244 exists without any filters.
if you've found something, that's great
can you enlighten us as to which of the 4 elements (home O/D and road O/D) is most important in your model?
definitely in the nba or nfl, i don't see a home favorite of 3, 7, 10 or a home underdog of 3,6 as the same situations at all...
in nba, it gets a bit more complicated as favorite bias seems to be a big factor i.e. faves have done very well ATS the last x seasons.
The model considers scoring margin and averages for the performance at home and away.
Afterwards you compare the measured differences to the actual line.
If it is possible to include a public bias I presume that the value of this model will increase and maybe can be something tom emulate for other sports.
Here is a chart of my predictive model
Model 1: http://prntscr.com/oj66os
Model 2: http://prntscr.com/oj66za
I may be reading this wrong, but nothing about this appears to be predictive. It appears to be retrodictive.
You are right retrodictive it is.
If I was running a sportsbook, I would give everyone a free copy of killer sports, or whatever "trends/research" db of final scores that I could. That shit is about as useful as the board above the roulette wheel.
thats true if you analyze data by arbitrary/random stats. it is more useful for comparison of widespread effects, even if the lines are not accurate
mathematically, home field advantage jumps around like crazy between teams (and in all sports)...... but i think that's noise......
you need to look at home court advantage over a 5 year period or something like that.
and one that always made sense but not sure if it still exists (or even actually existed) is high altitude home teams (salt lake or denver in college basketball. mexican mens soccer team at azteca stadium)......... also, some ideas about playing in miami and partying the night before. or vegas for hockey.
OP, i don't think HFA is so inefficient that you can just say NBA home court is undervalued (or overvalued)... i think it needs to be situational or filtered somehow. or combined with other ideas (like home field in NFL is more or less important in very cold weather).
and of course then you have complications. a very snowy NFL game will have much lower score therefore that might look like much less HFA but it's really it's just a much lower variance game.
You could set a regression using (home team statistic less visiting team statistic) as the independent variable and the dependent variable being margin of victory. use only home team games. The regression will produce a constant and a coefficient for the dependent variable. The constant is the HFA.
The HFA is real, and there is an assumed home field advantage in the point spreads, which is most likely too high. This explains why home teams win more games straight up, but lose more ATS.
There are a couple different ways to measure HFA.
The simplest, where teams play an even number of home and away games during the regular season is to 1. Look at overall win percentage, and 2. Overall median margin of victory. In NFL, the home teams win outright about 59% (it's been a few years since I've gone over this, so my numbers might be stale) and the median margin of victory was 3 (with few bigger wins than smaller wins or losses), so you might tsay the median spread is about -3 48%. If you measure HFA by spread and moneyline, they should convert correctly. A no-vig price of -3 +108 is pretty close to a no-vig moneyline of -145, so NFL passes the sanity check there.
You saw Pom used only conference games in college sports. This makes sure you are using the same number of home and away games. It also removes the creampuff bias, where big teams not only play extra home games, but play them against weaker opponents.
The more interesting question is how HFA is reflected in-game. This is constantly modeled incorrectly in live, and if you can figure it out, you'll find a lot of value in live (and h2) betting.
Thx for the insight.
In sports where wind and altitude plays a big role (MLB, NFL) I can see a pattern for overvaluing Home Teams.
I tried to correlate the ML data with he ATS data and found some interesting trends.
Is there any site you suggest where I can see the money on ML and ATS vice versa?
Why median and not average margin of victory?
Spread betting is based on medians. The average home margin of victory is higher than the median. If the mean was 3.2, and the median was 3 with 55% less, 45% over, would you bet a home team at -3 Even Money if two equal teams were playing?
Got it thanks for the lesson. I assume if the sample size is large enough for example NBA basketball, the mean and median are very close? However in the NFL a couple of outlier games can really skew the mean.
Phil Steele for NCAA football. Kenny white as well
NBA is a little weirder. I think the Median result is 4, with a very even split. The mean result is about 3.1 (sample nearly 20k games, but a few years dated). If you bet on the mean at even money, you would bet every road team at +3, and be disappointed that they covered at less than a 47% clip.
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