Beyond the Box Score Glossary (UPDATED)

Alright, it’s time to update the Glossary.  As I said before, my goal is to get more and more people interested in these weird stats that I've been using for the 'Beyond the Box Score' bits.  I will walk through the stats and explain them all in one place...and then link to this post in each future post.

Most of these stats are culled directly from Football Outsiders.  Some I've modified, some I've created out of thin air.

Success Rate

Success Rate is the standard for a lot of the stats I use.  It’s a slightly-altered version of a Football Outsiders measure.  On any given play, there’s a roughly 44% chance of ‘success’. Here are the rules according to down:

1st Down: 50% of necessary yardage.  If it's 1st-and-10, you need 5 yards for 'success'.  Football Outsiders use 40% for 1st down, but with the games I've entered, that led to a 1st down success rate of about 51%.  Bumping the requirements to 50% led to the 44% rate for which I was aiming.

2nd Down: 65% of necessary yardage (rounded up to the nearest yard, of course).  If it's 2nd-and-10, you need 7 yards for 'success'.  2nd-and-15?  10 yards.  This makes sense, really, because to succeed regularly on 3rd downs, you need to stay at 3rd-and-5 or less.  Getting most of the way there on 2nd downs sets you up infinitely better for 3rd down.

Football Outsiders uses 70%, but the success rate for that was around 42%.  Weakening the requirements slightly got me into the range I was looking for.

3rd and 4th Downs: 100% of necessary yardage.  I figure this requires no explanation.

The ways I've been using Success Rate to date are pretty widespread.  I can look at a team's Success Rate by quarter or down or game scenario (ahead, behind, tied, close, etc.), and it tells a pretty interesting story.  I'm probably not dealing with enough of a sample size to draw any serious conclusions, but a team's trends for Success Rates don't tend to stray drastically from game to game.  To understand what I mean, look at the first OU-Mizzou game.  Coming into the game, OU's strongest quarter was Q1.  In Q1 of OU-MU, OU held a 62.5%-35.7% advantage.  Meanwhile, Q3 has been Mizzou's strength all season, and sure enough, they held a 64.0%-46.7% advantage in Q3.  

When a team’s success rate varies wildly in any one game, it will likely significantly impact the outcome of the game.  For instance, throughout the season, Arkansas’ best quarter, both offensively and defensively, was by far Q2.  In Q2 of the Cotton Bowl, Mizzou held a 50%-25% advantage, and while the score at halftime was only 14-0 Mizzou, the Success Rate outcome of Q2 was a sign of foreboding for the Hogs.

Success Rate doesn't directly lead to points—points are just as much determined by lucky bounces, turnovers, clutch individual plays, etc.  However, Success Rate can tell you how a game will flow, and who will probably be creating better opportunities in specific situations, and over the long haul that can probably tell you who's winning and who's losing.  I like this stat a lot, which is pretty obvious considering how much I use it.  It’s like the football version of on-base percentage.  It’s not directly responsible for points, but you can’t really score without it.

Other uses of Success Rate: QB Success Rate (how a QB directs the offense as a whole, counting both runs and passes), Run Success Rate, Receiver Success Rate, etc.

Defensive Success Rate

This one's pretty easy: if it's a 'successful' play for the offense, it's an 'unsuccessful' play for the defense.  Defensive Success Rates are simply the inverse of Offensive Success Rates.  I break this out by position/unit a lot, looking at DL, LB, and DB success rates.

Points Per Play

If success rates are like on-base percentage, then Points Per Play (PPP) are like slugging percentage.  Probably the coolest thing I’ve come up with completely independent of Football Outsiders or anything else is the EqPts measure.  It’s based on the average number of points that could be expected when the offense is on any specific yard line.

This is the average points per possession for a play taking place at a given yard line.  Possessions with a play at your own 1-yard line average about 0.9 points per possession.  Possessions with a play at your opponents' 1-yard line average about 6.0 points per possession.  Your likelihood of scoring doesn't go up much anywhere between your goal line and 20, but it goes up quite a bit between your 30 and 40...and then again between your opponents' 30 and 40.  The slope increases the closer you get to the goalline, which makes sense.

As you see, gains in certain areas of the field are worth different amounts.  I did the same thing for each down.

So a 5-yard gain on 3rd-and-5 from your opponent’s 39 would only be worth about .19 points on the first graph, but it’s worth a huge 2.24 points on the second one.

Looking at things this way gives you a lot better indication of who accounted for a team’s points than yards or TDs or yards per carry.  It measures not only how many yards you gain at any one time, but also how important those yards are toward points actually being scored.

Let’s look at the Big 12’s top rushers for 2007 (yardage stats are pre-bowl):

• Jamaal Charles (UT): 1458 yards, 16 TDs, 6.3 ypc
• Dantrell Savage (OSU): 1172 yards, 8 TDs, 5.9 ypc
• James Johnson (KSU): 1106 yards, 12 TDs, 6.4 ypc
• Brandon McAnderson (KU): 1050 yards, 16 TDs, 6.0 ypc
• Marlon Lucky (NU): 1019 yards, 9 TDs, 4.9 ypc
• Hugh Charles (CU): 989 yards, 8 TDs, 5.3 ypc
• Allen Patrick (OU): 927 yards, 8 TDs, 5.8 ypc
• Jake Sharp (KU): 788 yards, 7 TDs, 5.7 ypc
• DeMarco Murray (OU): 764 yards, 13 TDs, 6.0 ypc
• Tony Temple (MU): 758 yards, 8 TDs, 4.7 ypc

Now let’s look at those runners based on success rates:

• J. Charles: .459 PPP, 49.1% success rate
• Savage: .328 PPP, 57.5%
• J. Johnson: .463 PPP, 50.9%
• McAnderson: .493 PPP, 54.3%
• Lucky: .280 PPP, 47.1%
• H. Charles: .309 PPP, 44.1%
• Patrick: .338 PPP, 46.8%
• Sharp: .376 PPP, 58.7%
• Murray: .459 PPP, 52.8%
• Temple: .292 PPP, 40.7% (obviously this went up big-time after the Cotton Bowl)


So if we are using an On-Base % type of number (success rate) and a Slugging % type of number (PPP), if we combined them, would it result in an OPS (On-Base + Slugging) type of number?  OPS is one of the most useful offensive measures in baseball...what would S&P tell us about football?  Again, let’s look at the Big 12’s top RBs.

• Brandon McAnderson: 1.04 S&P
• DeMarco Murray: 0.99 S&P
• James Johnson: 0.97 S&P
• Jake Sharp: 0.96 S&P
• Jamaal Charles: 0.95 S&P
• Dantrell Savage: 0.90 S&P
• Allen Patrick: 0.81 S&P
• Marlon Lucky: 0.75 S&P
• Hugh Charles: 0.75 S&P
• Tony Temple: 0.70 S&P

As with OPS, there are a couple different ways to get to the top of the rankings.  If you’re amazingly consistent (like McAnderson and Sharp) or explosive (Murray, Johnson), you can put up good S&P numbers.  This S&P measure doesn’t favor any specific type of runner.

The S&P concept can be used just as effectively for receivers.

Line Yards

So enough about the skill position guys.  What about the hosses in the middle?  There are plenty of stats to measure the effectiveness of a QB, RB, or WR/TE.  Even defensive players have quite a few to go by--tackles, sacks, defensive success rate, etc.  However, the play of the O-line or D-line as a whole goes a long way toward determining the outcome of a game, and there really aren't many measures for this.  One of the better ones, particularly for rushing plays, is Line Yards.

Here are the rules, via Football Outsiders:

• For a play that resulted in negative yards, the O-line is given 120% of the effort (i.e. a 3-yard loss would be a 3.6-yard loss for the O-line).

• For a play that resulted in a 0-4 yard gain, the O-line is granted 100%.

• For a play that resulted in a 5-10 yard gain, the O-line is granted 50% of the yards over 4 (i.e. an 8-yard gain would be a 6-yard gain for the O-line).

• For a play that resulted in a 10+ yard gain, the O-line get no extra credit—by that point, the runner is into the secondary, and the line won’t get much chance to block. Therefore (if the math in my head is correct), the most credit an O-line can get is 7.5 yards.

You get the idea here.  If a runner gets 10 yards or 50 yards, the O-line did its job equally well because at some point it's all on the runner.  Linemen are a bit too big and slow to follow all the way down the field.

My own theory is, this works a lot better for runs than passes since, obviously, the O-line isn't allowed to run down the field and block for receivers until the ball is actually thrown, and in most cases (some screen passes being the obvious exception) the ball is thrown pretty far away from the line.  So I use Line Yards to judge run blocking and...

Sack Rate judge pass blocking.  Both of these measures are flawed--if you have a quick, elusive QB who's good at escaping a collapsing pocket, you're probably going to have a lower sack rate than if you have a sedentary guy back there.  However, this measure is as good as anything else.

This one's pretty easy:

Sack Rate = (sacks) / (sacks + passes).

There you go.  One thing to keep in mind here is that, as a whole, there's a higher sack rate on passing downs (like 2nd-and-10 or 3rd-and-7) than on non-passing downs, so when I’m comparing teams, I tend to look at both.  And you can compare offensive and defensive lines

Turnover Costliness

Each turnover is assigned two values: 1) the point value (see below) of the offense's field position at the time of the turnover, and 2) the point value of the resulting starting field position for the opposition.

Turnover Costliness = (0.75*the higher of the two values)+(0.25*the lower of the two).

I previously had a factor in here regarding closeness of the game, and I'm sure I will again, but for now this is what I'm working with.


So there you have it.  As I come up with new stats and measures, I'll just add them to this post.  This way I have a specific reference for all future BTBS posts.  If this interests you at all, or if you come up with other things I can look at, feel free to comment below.  I'm all ears.

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