The response to your question could vary, depending on who replies. And this is for good reason and not due to the lack of information, opinions, theories, matrixes and valuation systems available to the topic.<?xml:namespace prefix = o ns = "urn:schemas-microsoft-com
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In my personal opinion, home field advantage (HFA) can’t really be generalized or covered by one single universal valuation (i.e., points). But rather, HFA varies depending on the specific teams and scenarios that are involved.<o></o>
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Ask yourself, is the HFA the same across the entire NFL? Of course not. Traditionally certain venues like have offered higher degrees of HFA. So not only does the degree of HFA vary from venue to venue, but the reasons for the higher HFA varies as well. A reason like a higher degree of fan support (Seattle’s 12 man) is just one example. Think of the others like a higher degree of intimidation (the rowdiness of Baltimore and Philadelphia), actual physical barriers (playing at high altitude in Denver, or any team having to cross two time zones to get to their destination), or harsher environmental elements (the cold and snow at places like Green Bay or Buffalo).
<o></o>
Speaking of physical barriers, occasionally an RX poster named JackofClubs will stop by my weekly threads to remind us all of the “Shoe Rule”, which is a system play ATS going against any road team that requires a change of shoes to play on that weeks surface (i.e., a turf team playing on grass or vice versa). Although the Shoe Rule is not doing as well this year as it has in the past, the teory is solid and is indeed something that will alter the degree of HFA. All you need to do is think back to the “Greatest Show On Turf” days when the Rams didn’t play quite as well on the slower natural turf. Or think about some of the historical struggles we’ve seen from grass teams like Denver or Green Bay on the road playing on artificial turf.<o></o>
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So what does all this mean? How do we convert this into points?<o></o>
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The simplest approach is to apply a universal point value like 2 points or 3 point to all HFA. A lightly more involved approach is average the difference between a team's home performance and it's away performance in terms of points per game. This is a two step process. Step one is to calculate the home / away winning margin differentials for both the home and away teams involved. Step two is to subtract that difference and then average it with the league average.<o></o>
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Much more involved approaches use sophisticated mathematical models.<o></o>
<o></o>
The table below is from the Football Prediction Network shows the average of year-end league averages in several metrics split according to performance at home and on the road:
<TABLE border=1><TBODY><TR><TD align=middle colSpan=3>Avg. Performance of League, 1996-2006</TD></TR>
<TR><TD>Stat</TD><TD>Home</TD><TD>Away</TD></TR>
<TR><TD>Run Eff.</TD><TD>4.103</TD><TD>3.9956</TD></TR>
<TR><TD>Pass Eff.</TD><TD>6.0151</TD><TD>5.7526</TD></TR>
<TR><TD>Sack Rate Allowed</TD><TD>6.5962%</TD><TD>6.9897%</TD></TR>
<TR><TD>Punt Ret.</TD><TD>9.5288</TD><TD>9.1585</TD></TR>
<TR><TD>Kick Ret.</TD><TD>22.084</TD><TD>21.479</TD></TR>
<TR><TD>3rd Down Conv.</TD><TD>38.656%</TD><TD>37.005%</TD></TR>
<TR><TD>Pen. 1st Downs Given</TD><TD>1.667</TD><TD>1.5091</TD></TR>
<TR><TD>Pen. Yds. Given</TD><TD>52.257</TD><TD>55.936</TD></TR>
<TR><TD>Int. Rate</TD><TD>2.8441%</TD><TD>3.1675%</TD></TR>
<TR><TD>Fum. Rate</TD><TD>3.1435%</TD><TD>3.1924%</TD></TR></TBODY></TABLE>
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The Football Prediction Network goes on to explain:<o></o>
<o></o>
As expected, home teams are consistently better, but the scale of those differences is small. The difference is run efficiency amounts to an extra 2 yards for every 30 attempts. For pass efficiency, it amounts to an extra 8 yards for every 30 attempts. The difference in interception rates doesn't even amount to a tenth of an interception for every 30 pass attempts. Yet these differences are somehow worth 2.7857 points to the home team and a 58.941% / 41.509% split of games.<o></o>
<o></o>
So to adjust stats for home field advantage, I used the same method as opponent adjustments. Instead of adjusting by the ratio of league average to opponent average, I use the ratio of league average to home/away average. In terms of season win totals, adjusting for home-field advantage increased the R<SUP>2</SUP> of the model (1996-2006 data) from 0.728 to 0.764. Adjusting the stats for home-field advantage does improve the statistics. Unfortunately, it does not significantly alleviate the problem of regression models classifying too many games as home team wins (a 1-2% decrease, still above 70%.
fficeffice" /><o></o><o
></o>In my personal opinion, home field advantage (HFA) can’t really be generalized or covered by one single universal valuation (i.e., points). But rather, HFA varies depending on the specific teams and scenarios that are involved.<o
></o><o
></o>Ask yourself, is the HFA the same across the entire NFL? Of course not. Traditionally certain venues like have offered higher degrees of HFA. So not only does the degree of HFA vary from venue to venue, but the reasons for the higher HFA varies as well. A reason like a higher degree of fan support (Seattle’s 12 man) is just one example. Think of the others like a higher degree of intimidation (the rowdiness of Baltimore and Philadelphia), actual physical barriers (playing at high altitude in Denver, or any team having to cross two time zones to get to their destination), or harsher environmental elements (the cold and snow at places like Green Bay or Buffalo).
<o
></o>Speaking of physical barriers, occasionally an RX poster named JackofClubs will stop by my weekly threads to remind us all of the “Shoe Rule”, which is a system play ATS going against any road team that requires a change of shoes to play on that weeks surface (i.e., a turf team playing on grass or vice versa). Although the Shoe Rule is not doing as well this year as it has in the past, the teory is solid and is indeed something that will alter the degree of HFA. All you need to do is think back to the “Greatest Show On Turf” days when the Rams didn’t play quite as well on the slower natural turf. Or think about some of the historical struggles we’ve seen from grass teams like Denver or Green Bay on the road playing on artificial turf.<o
></o><o
></o>So what does all this mean? How do we convert this into points?<o
></o><o
></o>The simplest approach is to apply a universal point value like 2 points or 3 point to all HFA. A lightly more involved approach is average the difference between a team's home performance and it's away performance in terms of points per game. This is a two step process. Step one is to calculate the home / away winning margin differentials for both the home and away teams involved. Step two is to subtract that difference and then average it with the league average.<o
></o><o
></o>Much more involved approaches use sophisticated mathematical models.<o
></o><o
></o>The table below is from the Football Prediction Network shows the average of year-end league averages in several metrics split according to performance at home and on the road:
<TABLE border=1><TBODY><TR><TD align=middle colSpan=3>Avg. Performance of League, 1996-2006</TD></TR>
<TR><TD>Stat</TD><TD>Home</TD><TD>Away</TD></TR>
<TR><TD>Run Eff.</TD><TD>4.103</TD><TD>3.9956</TD></TR>
<TR><TD>Pass Eff.</TD><TD>6.0151</TD><TD>5.7526</TD></TR>
<TR><TD>Sack Rate Allowed</TD><TD>6.5962%</TD><TD>6.9897%</TD></TR>
<TR><TD>Punt Ret.</TD><TD>9.5288</TD><TD>9.1585</TD></TR>
<TR><TD>Kick Ret.</TD><TD>22.084</TD><TD>21.479</TD></TR>
<TR><TD>3rd Down Conv.</TD><TD>38.656%</TD><TD>37.005%</TD></TR>
<TR><TD>Pen. 1st Downs Given</TD><TD>1.667</TD><TD>1.5091</TD></TR>
<TR><TD>Pen. Yds. Given</TD><TD>52.257</TD><TD>55.936</TD></TR>
<TR><TD>Int. Rate</TD><TD>2.8441%</TD><TD>3.1675%</TD></TR>
<TR><TD>Fum. Rate</TD><TD>3.1435%</TD><TD>3.1924%</TD></TR></TBODY></TABLE>
<o
></o><o
></o><o
></o>The Football Prediction Network goes on to explain:<o
></o><o
></o>As expected, home teams are consistently better, but the scale of those differences is small. The difference is run efficiency amounts to an extra 2 yards for every 30 attempts. For pass efficiency, it amounts to an extra 8 yards for every 30 attempts. The difference in interception rates doesn't even amount to a tenth of an interception for every 30 pass attempts. Yet these differences are somehow worth 2.7857 points to the home team and a 58.941% / 41.509% split of games.<o
></o><o
></o>So to adjust stats for home field advantage, I used the same method as opponent adjustments. Instead of adjusting by the ratio of league average to opponent average, I use the ratio of league average to home/away average. In terms of season win totals, adjusting for home-field advantage increased the R<SUP>2</SUP> of the model (1996-2006 data) from 0.728 to 0.764. Adjusting the stats for home-field advantage does improve the statistics. Unfortunately, it does not significantly alleviate the problem of regression models classifying too many games as home team wins (a 1-2% decrease, still above 70%.
More from the Football Prediction Network (http://footballpredictionnetwork.blogspot.com/2007/07/value-of-home-field-advantage-part-33.html):
One question that's been bugging me for the last few months is why home teams did so well in 2005 and so poorly in 2006. When you consider the Saints home games in 2005, the numbers seem even stranger. Is the variance for all types of matchups, or is there one specific type of matchups that has more inherent variance? Since 2002, when the league expanded to 32 teams, the scheduling for the NFL became very simplified with 16 teams in each conference and 4 teams in each division. Now, each team in the AFC plays the teams in one division of the NFC once every four years and vice versa. So interconference schedules for each team changed drastically in terms of opponent quality and regions of the country visited (and thus climates experienced). The same holds true, though to a lesser extent, for intraconference, interdivision play, as teams still have to play at least one team from each other division within the conference a year.
So the hypothesis is as follows: The scheduling procedure implemented by the NFL since the league's expansion to 32 teams has made home field advantage less stable from year-to-year, increasing the varaince of home team winning percentage and average result (home team points - away team points). Interconference games have the most year-to-year variance, followed by interdivisional games. Intradivisional games should have the least variance. The interconference games are largely responsible for the aberrant numbers for the 2005 and 2006 seasons.
<STYLE type=text/css>.nobrtable br { display: none }</STYLE>
<TABLE border=1><TBODY><TR><TD>Home Win % (mean/std. dev.)</TD><TD>Interconference</TD><TD>Interdivision</TD><TD>Intradivision</TD></TR>
<TR><TD>1994-2001</TD><TD>59.199/4.74</TD><TD>60.466/4.42</TD><TD>58.137/3.57</TD></TR>
<TR><TD>2002-2006</TD><TD>60.625/7.19</TD><TD>56.875/2.40</TD><TD>56.25/2.21</TD></TR>
<TR><TD>Avg Result
(mean/std. dev.)</TD><TD>Interconference</TD><TD>Interdivision</TD><TD>Intradivision</TD></TR>
<TR><TD>1994-2001</TD><TD>3.0489/1.8585</TD><TD>2.0519/0.910</TD><TD>2.7675/1.228</TD></TR>
<TR><TD>2002-2006</TD><TD>2.6875/2.5992</TD><TD>2.9896/0.824</TD><TD>2.0354/0.816</TD></TR>
</TBODY></TABLE>
As predicted, the standard deviation for home team winning percentage increased overall, but by category, only the standard deviation for interconference games increased, though it did so significantly. Before the realignment in 2002, each division had 4 or 5 or 6 teams, so the number of intradivisional games played by each team was not always even. Even in 1995-1998, when each of the six divisions had five teams, interdivisional games were tougher to schedule. Of the 8 non-intradivisional games, 4 were interconference, and 4 were interdivisional, meaning no team would be playing against every team in any other division. So for example, though the AFC East might be matched up with the AFC Central, the Bills might get an easier schedule against than the Dolphins because the Bills get to play the Bengals. The scheduling might also have been tooled around with to give worse teams easier schedules, resulting in some teams not meeting each other for many years, whereas the new system guarantees that won't happen. The closer teams are in quality, the more variance one would expect in the outcome. Therefore, it makes sense that the new scheduling formula decreases variance for interdivision and intradivision games.
Nevertheless, in both time periods, variance was highest for interconference games, followed by interdivision games and then intradivision games. Suprisingly, home field advantage seems to have lost some value since the realignment. Fewer games are won by the home team and by fewer points. With fewer teams in the division, intradivisional games might involve more parity and thus more variance in outcomes. The slight uptick in home team winning percentage for interconference games might have to do with the imbalance between the conferences. Though fewer interdivisional games are won by the home team, the average result has increased in favor of the home team by nearly a whole point. The converse is true for interconference games. In both cases, I'm not really sure why that happens with the average result. At any rate, home field advantage ain't what it used to be, so I might have to go back and rerun experiments training only on 2002 and beyond.
<STYLE type=text/css>.nobrtable br { display: none }</STYLE>
<TABLE border=1><TBODY><TR><TD></TD><TD align=middle colSpan=3>All Games</TD><TD align=middle colSpan=3>Interconference</TD></TR>
<TR><TD>Year</TD><TD>Games</TD><TD>Home Win%</TD><TD>Avg Result</TD><TD>Games</TD><TD>Home Win%</TD><TD>Avg Result</TD></TR>
<TR><TD>1994</TD><TD>224</TD><TD>0.57143</TD><TD>1.4598</TD><TD>52</TD><TD>0.51923</TD><TD>0.05769</TD></TR>
<TR><TD>1995</TD><TD>240</TD><TD>0.6</TD><TD>2.025</TD><TD>60</TD><TD>0.65</TD><TD>3.0667</TD></TR>
<TR><TD>1996</TD><TD>240</TD><TD>0.62083</TD><TD>3.7208</TD><TD>60</TD><TD>0.6</TD><TD>3.4833</TD></TR>
<TR><TD>1997</TD><TD>240</TD><TD>0.60417</TD><TD>2.7958</TD><TD>60</TD><TD>0.61667</TD><TD>3.9167</TD></TR>
<TR><TD>1998</TD><TD>240</TD><TD>0.62917</TD><TD>3.5042</TD><TD>60</TD><TD>0.61667</TD><TD>3.4333</TD></TR>
<TR><TD>1999</TD><TD>248</TD><TD>0.59677</TD><TD>3.0645</TD><TD>60</TD><TD>0.56667</TD><TD>2.1167</TD></TR>
<TR><TD>2000</TD><TD>248</TD><TD>0.55645</TD><TD>2.8226</TD><TD>60</TD><TD>0.63333</TD><TD>6.4833</TD></TR>
<TR><TD>2001</TD><TD>248</TD><TD>0.55242</TD><TD>2.0444</TD><TD>60</TD><TD>0.53333</TD><TD>1.8333</TD></TR>
<TR><TD>2002</TD><TD>256</TD><TD>0.57813</TD><TD>2.2461</TD><TD>64</TD><TD>0.65625</TD><TD>3.9375</TD></TR>
<TR><TD>2003</TD><TD>256</TD><TD>0.61328</TD><TD>3.5313</TD><TD>64</TD><TD>0.65625</TD><TD>4.125</TD></TR>
<TR><TD>2004</TD><TD>256</TD><TD>0.56641</TD><TD>2.5078</TD><TD>64</TD><TD>0.5625</TD><TD>2</TD></TR>
<TR><TD>2005</TD><TD>256</TD><TD>0.58984</TD><TD>3.6484</TD><TD>64</TD><TD>0.65625</TD><TD>4.9219</TD></TR>
<TR><TD>2006</TD><TD>256</TD><TD>0.53125</TD><TD>0.84766</TD><TD>64</TD><TD>0.5</TD><TD>-1.5469</TD></TR>
<TR><TD></TD><TD align=middle colSpan=3>Interdivision</TD><TD align=middle colSpan=3>Intradivision</TD></TR>
<TR><TD>Year</TD><TD>Games</TD><TD>Home Win%</TD><TD>Avg Result</TD><TD>Games</TD><TD>Home Win%</TD><TD>Avg Result</TD></TR>
<TR><TD>1994</TD><TD>68</TD><TD>0.64706</TD><TD>3.3824</TD><TD>104</TD><TD>0.54808</TD><TD>0.90385</TD></TR>
<TR><TD>1995</TD><TD>60</TD><TD>0.56667</TD><TD>1.3</TD><TD>120</TD><TD>0.59167</TD><TD>1.8667</TD></TR>
<TR><TD>1996</TD><TD>60</TD><TD>0.63333</TD><TD>2.2</TD><TD>120</TD><TD>0.625</TD><TD>4.6</TD></TR>
<TR><TD>1997</TD><TD>60</TD><TD>0.61667</TD><TD>1.4333</TD><TD>120</TD><TD>0.59167</TD><TD>2.9167</TD></TR>
<TR><TD>1998</TD><TD>60</TD><TD>0.66667</TD><TD>2.6167</TD><TD>120</TD><TD>0.61667</TD><TD>3.9833</TD></TR>
<TR><TD>1999</TD><TD>58</TD><TD>0.60345</TD><TD>3.1379</TD><TD>130</TD><TD>0.60769</TD><TD>3.4692</TD></TR>
<TR><TD>2000</TD><TD>58</TD><TD>0.55172</TD><TD>1.2586</TD><TD>130</TD><TD>0.52308</TD><TD>1.8308</TD></TR>
<TR><TD>2001</TD><TD>58</TD><TD>0.55172</TD><TD>1.0862</TD><TD>130</TD><TD>0.56154</TD><TD>2.5692</TD></TR>
<TR><TD>2002</TD><TD>96</TD><TD>0.54167</TD><TD>2.1563</TD><TD>96</TD><TD>0.5625</TD><TD>1.2083</TD></TR>
<TR><TD>2003</TD><TD>96</TD><TD>0.60417</TD><TD>3.7083</TD><TD>96</TD><TD>0.59375</TD><TD>2.9583</TD></TR>
<TR><TD>2004</TD><TD>96</TD><TD>0.57292</TD><TD>3.0833</TD><TD>96</TD><TD>0.5625</TD><TD>2.2708</TD></TR>
<TR><TD>2005</TD><TD>96</TD><TD>0.57292</TD><TD>3.8646</TD><TD>96</TD><TD>0.5625</TD><TD>2.5833</TD></TR>
<TR><TD>2006</TD><TD>96</TD><TD>0.55208</TD><TD>2.1354</TD><TD>96</TD><TD>0.53125</TD><TD>1.1563</TD></TR>
</TBODY></TABLE>
Now, let's look at how the numbers break down by season. In 2005, although 58.98% of games are won by the home team, which is about average, the average result is very high at 3.6484. Only 1996 had a higher average result, so it's at the extremes of what's been observed before. The interconference games had an average result of 4.9219. On average, the home team won those games by nearly 5 points, which is very high, but it is still within what has been observed before. In 2000, the average result was 6.48333. The average result of interdivisional games was the highest in 2005 at 3.8646, while the home field advantage in intradivisional games was slightly below average that year. In 2006, interconference games made all the difference. Only 50% of the games were won by the home team, but the average result was actually in favor of the away team at -1.5469. The numbers for the intraconference games, while well below average, did not set any record lows. Given this data, it is reasonably safe to say that the year-to-year variance in home field advantage is largely due to interconference games.
In theory, what's happening is that as interconference matchups are rotated, strong teams are getting matched up with weak opponents. So some of this variance should be predictable. In 2006, only 40.63% of home teams in interconference games had better records than their opponents in the previous season. In 2002-2005, the numbers were 46.88%, 48.44%, 43.75%, and 43.75% respectively. The correlation of this stat to the proportion of interconference games won by the home team in that year is very strong, 0.82427, though 2005 was still better than expected, given 2004. Five data points is too small to reasonably use linear regression, but we can still take a guess at how 2007 will turn out. It turns out that the 2007 stat matches 2003 at 48.44%, so expect home field advantage to return to at least normal levels in 2007.
So how does a prediction system like the spread handle interconference and interdivision games? Since 2002, the spread has had 63.75%, 63.96%, and 66.25% accuracy on interconference, interdivision, and intradivision games respectively, while the home team was favored in 67.50%, 68.54% and 65.83% of those games. The standard deviations of percentages of favorites being home teams are 4.65%, 2.16%, and 2.74%. So the spread does seem to be sensitive to the variance but not strong enough. If similar numbers hold for my linear regression model, then perhaps better opponent adjustments are needed. Given that 75% of the season is played intraconference, I'm wondering if stats should be adjusted based on conference averages rather than league averages. I know they do similar things for baseball. It's something I'll tinker around with in the future. In short, I just traveled a long, long road for a maybe. As usual, answering one question led to several new questions popping up.
One question that's been bugging me for the last few months is why home teams did so well in 2005 and so poorly in 2006. When you consider the Saints home games in 2005, the numbers seem even stranger. Is the variance for all types of matchups, or is there one specific type of matchups that has more inherent variance? Since 2002, when the league expanded to 32 teams, the scheduling for the NFL became very simplified with 16 teams in each conference and 4 teams in each division. Now, each team in the AFC plays the teams in one division of the NFC once every four years and vice versa. So interconference schedules for each team changed drastically in terms of opponent quality and regions of the country visited (and thus climates experienced). The same holds true, though to a lesser extent, for intraconference, interdivision play, as teams still have to play at least one team from each other division within the conference a year.
So the hypothesis is as follows: The scheduling procedure implemented by the NFL since the league's expansion to 32 teams has made home field advantage less stable from year-to-year, increasing the varaince of home team winning percentage and average result (home team points - away team points). Interconference games have the most year-to-year variance, followed by interdivisional games. Intradivisional games should have the least variance. The interconference games are largely responsible for the aberrant numbers for the 2005 and 2006 seasons.
<STYLE type=text/css>.nobrtable br { display: none }</STYLE>
<TABLE border=1><TBODY><TR><TD>Home Win % (mean/std. dev.)</TD><TD>Interconference</TD><TD>Interdivision</TD><TD>Intradivision</TD></TR>
<TR><TD>1994-2001</TD><TD>59.199/4.74</TD><TD>60.466/4.42</TD><TD>58.137/3.57</TD></TR>
<TR><TD>2002-2006</TD><TD>60.625/7.19</TD><TD>56.875/2.40</TD><TD>56.25/2.21</TD></TR>
<TR><TD>Avg Result
(mean/std. dev.)</TD><TD>Interconference</TD><TD>Interdivision</TD><TD>Intradivision</TD></TR>
<TR><TD>1994-2001</TD><TD>3.0489/1.8585</TD><TD>2.0519/0.910</TD><TD>2.7675/1.228</TD></TR>
<TR><TD>2002-2006</TD><TD>2.6875/2.5992</TD><TD>2.9896/0.824</TD><TD>2.0354/0.816</TD></TR>
</TBODY></TABLE>
As predicted, the standard deviation for home team winning percentage increased overall, but by category, only the standard deviation for interconference games increased, though it did so significantly. Before the realignment in 2002, each division had 4 or 5 or 6 teams, so the number of intradivisional games played by each team was not always even. Even in 1995-1998, when each of the six divisions had five teams, interdivisional games were tougher to schedule. Of the 8 non-intradivisional games, 4 were interconference, and 4 were interdivisional, meaning no team would be playing against every team in any other division. So for example, though the AFC East might be matched up with the AFC Central, the Bills might get an easier schedule against than the Dolphins because the Bills get to play the Bengals. The scheduling might also have been tooled around with to give worse teams easier schedules, resulting in some teams not meeting each other for many years, whereas the new system guarantees that won't happen. The closer teams are in quality, the more variance one would expect in the outcome. Therefore, it makes sense that the new scheduling formula decreases variance for interdivision and intradivision games.
Nevertheless, in both time periods, variance was highest for interconference games, followed by interdivision games and then intradivision games. Suprisingly, home field advantage seems to have lost some value since the realignment. Fewer games are won by the home team and by fewer points. With fewer teams in the division, intradivisional games might involve more parity and thus more variance in outcomes. The slight uptick in home team winning percentage for interconference games might have to do with the imbalance between the conferences. Though fewer interdivisional games are won by the home team, the average result has increased in favor of the home team by nearly a whole point. The converse is true for interconference games. In both cases, I'm not really sure why that happens with the average result. At any rate, home field advantage ain't what it used to be, so I might have to go back and rerun experiments training only on 2002 and beyond.
<STYLE type=text/css>.nobrtable br { display: none }</STYLE>
<TABLE border=1><TBODY><TR><TD></TD><TD align=middle colSpan=3>All Games</TD><TD align=middle colSpan=3>Interconference</TD></TR>
<TR><TD>Year</TD><TD>Games</TD><TD>Home Win%</TD><TD>Avg Result</TD><TD>Games</TD><TD>Home Win%</TD><TD>Avg Result</TD></TR>
<TR><TD>1994</TD><TD>224</TD><TD>0.57143</TD><TD>1.4598</TD><TD>52</TD><TD>0.51923</TD><TD>0.05769</TD></TR>
<TR><TD>1995</TD><TD>240</TD><TD>0.6</TD><TD>2.025</TD><TD>60</TD><TD>0.65</TD><TD>3.0667</TD></TR>
<TR><TD>1996</TD><TD>240</TD><TD>0.62083</TD><TD>3.7208</TD><TD>60</TD><TD>0.6</TD><TD>3.4833</TD></TR>
<TR><TD>1997</TD><TD>240</TD><TD>0.60417</TD><TD>2.7958</TD><TD>60</TD><TD>0.61667</TD><TD>3.9167</TD></TR>
<TR><TD>1998</TD><TD>240</TD><TD>0.62917</TD><TD>3.5042</TD><TD>60</TD><TD>0.61667</TD><TD>3.4333</TD></TR>
<TR><TD>1999</TD><TD>248</TD><TD>0.59677</TD><TD>3.0645</TD><TD>60</TD><TD>0.56667</TD><TD>2.1167</TD></TR>
<TR><TD>2000</TD><TD>248</TD><TD>0.55645</TD><TD>2.8226</TD><TD>60</TD><TD>0.63333</TD><TD>6.4833</TD></TR>
<TR><TD>2001</TD><TD>248</TD><TD>0.55242</TD><TD>2.0444</TD><TD>60</TD><TD>0.53333</TD><TD>1.8333</TD></TR>
<TR><TD>2002</TD><TD>256</TD><TD>0.57813</TD><TD>2.2461</TD><TD>64</TD><TD>0.65625</TD><TD>3.9375</TD></TR>
<TR><TD>2003</TD><TD>256</TD><TD>0.61328</TD><TD>3.5313</TD><TD>64</TD><TD>0.65625</TD><TD>4.125</TD></TR>
<TR><TD>2004</TD><TD>256</TD><TD>0.56641</TD><TD>2.5078</TD><TD>64</TD><TD>0.5625</TD><TD>2</TD></TR>
<TR><TD>2005</TD><TD>256</TD><TD>0.58984</TD><TD>3.6484</TD><TD>64</TD><TD>0.65625</TD><TD>4.9219</TD></TR>
<TR><TD>2006</TD><TD>256</TD><TD>0.53125</TD><TD>0.84766</TD><TD>64</TD><TD>0.5</TD><TD>-1.5469</TD></TR>
<TR><TD></TD><TD align=middle colSpan=3>Interdivision</TD><TD align=middle colSpan=3>Intradivision</TD></TR>
<TR><TD>Year</TD><TD>Games</TD><TD>Home Win%</TD><TD>Avg Result</TD><TD>Games</TD><TD>Home Win%</TD><TD>Avg Result</TD></TR>
<TR><TD>1994</TD><TD>68</TD><TD>0.64706</TD><TD>3.3824</TD><TD>104</TD><TD>0.54808</TD><TD>0.90385</TD></TR>
<TR><TD>1995</TD><TD>60</TD><TD>0.56667</TD><TD>1.3</TD><TD>120</TD><TD>0.59167</TD><TD>1.8667</TD></TR>
<TR><TD>1996</TD><TD>60</TD><TD>0.63333</TD><TD>2.2</TD><TD>120</TD><TD>0.625</TD><TD>4.6</TD></TR>
<TR><TD>1997</TD><TD>60</TD><TD>0.61667</TD><TD>1.4333</TD><TD>120</TD><TD>0.59167</TD><TD>2.9167</TD></TR>
<TR><TD>1998</TD><TD>60</TD><TD>0.66667</TD><TD>2.6167</TD><TD>120</TD><TD>0.61667</TD><TD>3.9833</TD></TR>
<TR><TD>1999</TD><TD>58</TD><TD>0.60345</TD><TD>3.1379</TD><TD>130</TD><TD>0.60769</TD><TD>3.4692</TD></TR>
<TR><TD>2000</TD><TD>58</TD><TD>0.55172</TD><TD>1.2586</TD><TD>130</TD><TD>0.52308</TD><TD>1.8308</TD></TR>
<TR><TD>2001</TD><TD>58</TD><TD>0.55172</TD><TD>1.0862</TD><TD>130</TD><TD>0.56154</TD><TD>2.5692</TD></TR>
<TR><TD>2002</TD><TD>96</TD><TD>0.54167</TD><TD>2.1563</TD><TD>96</TD><TD>0.5625</TD><TD>1.2083</TD></TR>
<TR><TD>2003</TD><TD>96</TD><TD>0.60417</TD><TD>3.7083</TD><TD>96</TD><TD>0.59375</TD><TD>2.9583</TD></TR>
<TR><TD>2004</TD><TD>96</TD><TD>0.57292</TD><TD>3.0833</TD><TD>96</TD><TD>0.5625</TD><TD>2.2708</TD></TR>
<TR><TD>2005</TD><TD>96</TD><TD>0.57292</TD><TD>3.8646</TD><TD>96</TD><TD>0.5625</TD><TD>2.5833</TD></TR>
<TR><TD>2006</TD><TD>96</TD><TD>0.55208</TD><TD>2.1354</TD><TD>96</TD><TD>0.53125</TD><TD>1.1563</TD></TR>
</TBODY></TABLE>
Now, let's look at how the numbers break down by season. In 2005, although 58.98% of games are won by the home team, which is about average, the average result is very high at 3.6484. Only 1996 had a higher average result, so it's at the extremes of what's been observed before. The interconference games had an average result of 4.9219. On average, the home team won those games by nearly 5 points, which is very high, but it is still within what has been observed before. In 2000, the average result was 6.48333. The average result of interdivisional games was the highest in 2005 at 3.8646, while the home field advantage in intradivisional games was slightly below average that year. In 2006, interconference games made all the difference. Only 50% of the games were won by the home team, but the average result was actually in favor of the away team at -1.5469. The numbers for the intraconference games, while well below average, did not set any record lows. Given this data, it is reasonably safe to say that the year-to-year variance in home field advantage is largely due to interconference games.
In theory, what's happening is that as interconference matchups are rotated, strong teams are getting matched up with weak opponents. So some of this variance should be predictable. In 2006, only 40.63% of home teams in interconference games had better records than their opponents in the previous season. In 2002-2005, the numbers were 46.88%, 48.44%, 43.75%, and 43.75% respectively. The correlation of this stat to the proportion of interconference games won by the home team in that year is very strong, 0.82427, though 2005 was still better than expected, given 2004. Five data points is too small to reasonably use linear regression, but we can still take a guess at how 2007 will turn out. It turns out that the 2007 stat matches 2003 at 48.44%, so expect home field advantage to return to at least normal levels in 2007.
So how does a prediction system like the spread handle interconference and interdivision games? Since 2002, the spread has had 63.75%, 63.96%, and 66.25% accuracy on interconference, interdivision, and intradivision games respectively, while the home team was favored in 67.50%, 68.54% and 65.83% of those games. The standard deviations of percentages of favorites being home teams are 4.65%, 2.16%, and 2.74%. So the spread does seem to be sensitive to the variance but not strong enough. If similar numbers hold for my linear regression model, then perhaps better opponent adjustments are needed. Given that 75% of the season is played intraconference, I'm wondering if stats should be adjusted based on conference averages rather than league averages. I know they do similar things for baseball. It's something I'll tinker around with in the future. In short, I just traveled a long, long road for a maybe. As usual, answering one question led to several new questions popping up.
not worth much anymore....heres an interesting article about new state of the art stadiums....worth a read...plus the sports guy is pretty funny too
http://sports.espn.go.com/espn/page2/story?page=simmons/partone/081121
http://sports.espn.go.com/espn/page2/story?page=simmons/partone/081121
Depends on the acoustics and how savvy the crowd is. Some teams really use it to maybe closer to 5-6 or 6 points. Others might be a 3 or a little less. Is it important to hear the qb? Do refs get intimidated? Do some teams amplify the crowd noise and pipe it through the pa system.
Some fans lose their voice if their team gets down 7-14. Some are quick to boo the home team. Lots of variables.
Some fans lose their voice if their team gets down 7-14. Some are quick to boo the home team. Lots of variables.
