The Pi-Rate Ratings

January 9, 2019

Advanced Basketball Statistics–Fun Stuff for Stats Buffs

This feature today is not for everybody. You have to be a stats fan for this one to be fun to read. Last year, we were asked to explain some of the advanced basketball metrics used today. Then, a couple weeks ago, we were asked again what certain metrics were. So, this will be an attempt to explain the basic advanced metrics and then to some degree how one might use this data to determine an approximate point spread difference.

If you are not familiar with advanced metrics in sports, it all started with baseball many decades ago. The legendary general manager of the then Brooklyn Dodgers, Branch Rickey, was always many years ahead of his contemporaries. He had basically created the farm system for Minor League baseball in the late 1920’s, and he opened the game to African Americans and then created a pipeline in Latin America for the Dodgers to take advantage there. Around the same time, Rickey was looking for a statistical advantage to evaluating baseball players, using mathematics to find hidden gems of talent that might have been somewhat overlooked by the competition. This was 50 years before Money Ball. Rickey aligned with a mathematics genius by the name of Alan Roth, who had previously tried to show some of his ideas to other baseball owners, but none of these owners had an interest. Rickey was more than interested, and he hired Roth to work for the Dodgers about the same time as Jackie Robinson debuted in Brooklyn.

Roth was one of the first baseball statisticians to realize that RBIs were basically worthless as a stat. For two decades, he worked for the Dodgers charting where players hit balls, what pitches they hit, and who fielded or did not field balls. This technology would not come into the norm for another 40 years.

Many others presented statistical data for baseball through the 1960’s, 1970’s, and 1980’s. Some of these math experts wrote books, such as Earnshaw Cook and his great work called, Percentage Baseball. It is this book that I read many years ago that roped me into the world of advanced baseball statistics.

A lot of you reading this know who Billy Beane is and what “Money Ball” is. Let me clue you in on something. This was not the first big leap into computer-generated advanced baseball statistics. It wasn’t even the first attempt by the Oakland Athletics. Beane’s predecessor, Sandy Alderson, brought the computer big time into baseball, but you could argue that Earl Weaver with the Baltimore Orioles had a basic no frills database of his batters’ and pitchers’ successes and failures against the pitchers and hitters throughout the American League.

About the time that Money Ball had come out in book form, other mathematics experts began looking at different sports. Computer specialists had come up with somewhat successful algorithms to pick winners against the point spread in football, and one or two became quite wealthy until the state of Nevada banned their wagering for life.

In the late 1990’s, the NBA began looking for ways to take advantage of the numbers to maximize talent. Was it worth it to shoot the 3-pointer? Was it better to have a strong rebounding team that maybe didn’t shoot as well than a weaker rebounding team that shot better? What was the best number of minutes to play your star players, and the best number of minutes to play your second team players? Could statistics show enough consistency to partially answer these questions?

Of course, the questions can never fully be answered. Until computers can read the minds of humans, they can never determine if Stephen Curry may have strained his right shoulder lifting his amazing daughter up in the air earlier that day. The computer cannot determine if the star player had a little too much pizza the night before and didn’t get a good night sleep. There is missing data that will be discovered in the future because basketball analytics are far behind baseball in the evolutionary process.
Basketball is starting to catch up now that very expensive software exists in NBA gyms where multiple cameras are placed in the rafters of the arenas, which feed into a computer and can show teams where all 10 players were on the floor for each 1/100 of a second of the game. If the power forward was beaten for an easy jumper when the shooter came off a baseline screen, the computer records this. Within a few years, the game will become every bit as scientific as baseball, and you will see more Cal Tech and MIT grads working in front offices.
By now, you must realize that trying to explain all the advanced basketball metrics would be terribly boring and very difficult to do. I admit that I am not the authority on basketball metrics, but then I get paid for baseball analytics and not basketball analytics.

Here is a brief look at some of the advanced stats for basketball. If you are interested, you should be able to set up these formulas on a spreadsheet and then plug your team’s stats in and have some of the more popular advanced stats for the team you follow. You can even use these stats for lower levels (high school, middle school, youth league), but the formulas must be altered by an amount I cannot give you. There is a difference between NBA and college formulas, and there will be differences as you go down in experience. Some of it has to do with how many fouls it takes to put a team in the bonus and what that bonus is.
Let’s Begin
I must start with the most basic of advanced statistics. This first set of stats will give you a lot more than the basic statistics. They are called, “The Four Factors,” but they are used for both a team’s offense and a team’s defense, so it is really eight factors.

Credit here must be given to the very brilliant Cal Tech statistician Dean Oliver who wrote the number one book on basketball statistics, Basketball on Paper. It is required reading if this is your field of interest. Oliver capitalized on his data and sold it and himself to a handful of NBA teams, but the basketball media wasn’t ready for his ideas.

They scrutinized every move made through his recommendations, forcing the NBA teams to give in to their fans that bought into the media’s opinions. Of course, many of these media hacks cannot balance their own checkbooks, so their scrutiny comes without credibility.  I say this because I was once a media hack in a top 30 market who believed a lot of the preconceived misconceptions of sports.
The “Four Factors” (again eight factors since this is figured for the offense and the defense) are:

1. Effective Field Goal Percentage
2. Turnover Rate
3. Offensive Rebounding Rate
4. Free Throw Rate.

While these factors are still quite valid, they have been surpassed somewhat by more advanced data. For example, True Shooting Percentage is more detailed than EFg%.

Here are the easy calculations for the Four Factors.
1. Effective Field Goal Percentage
This stat adds three point shooting to two point shooting into one stat. A made three-pointer is worth 50% more than a made two-pointer. So, if you make 1/3 of your three-pointers, it is the same as making 50% of your two-pointers.

The formula for eFG% is: (Field Goals Made + (0.5* 3-pointers Made))/Field Goals Attempted.

Let’s say that Duke takes 58 total shots in a game. They make 26 of these shots, and 8 of them are three-pointers. The calculation would be:

(26 +(0.5*8))/58 which equals .517 or 51.7%.

It works the same for defense. Let’s say in the same game, Duke’s opponent took 57 shots and made 24 with 7 of them three-pointers.

(24+(0.5*7))/57 = .482 or 48.2%.

1A. True Shooting Percentage combines Effective Field Goal Percentage with foul shooting into one combined scoring stat. As you will see with the 4th factor, there is debate over how to use FT Rate properly.

The NBA formula for True Shooting Percentage is: Pts/(2*(FGA+(.44*FTA))) but this is the NBA formula. As I mentioned above, the formula for college basketball is a little different, and it has to do with different Free Throw rules in the two organizations.

For college, it is: Pts/(2*(FGA+(.465*FTA)))

Let’s look at this for an individual. Here are Steph Curry’s Shooting Stats for his last year at Davidson.

Curry scored 974 points in 2008-09. He took 687 shots from the field and 251 foul shots.

974/(2*(687+(.465*251)))= .606 or 60.6% which for a guard is outstanding.

Compare this to Kareem Abdul-Jabbar’s sophomore season at UCLA in 1966-67, when the NCAA made the mistake of banning the dunk following his dominant first year on the varsity.  Jabbar, known then by his birth name of Lew Alcindor, scored 870 points that year with 519 shots from the field and 274 foul shots.

870/(2*(519+(.465*274)))=.673 or 67.3%.

You can see that a dominant post player like Jabbar was worth more in shooting than a top outside shooter like Curry. This is a relative statement, but it is like saying Babe Ruth was worth more as a hitter than Ty Cobb.

2. Turnover Rate
This measures the rate at which a team commits a turnover or forces the opponent to commit a turnover. We will stick with team stats for now, because the formulas for individuals are a bit more complex.

The calculation for Tunover Rate is:

TO / (FGA + (0.44 * FTA) + TO) for NBA, and
TO/(FGA+(.465*FTA+TO) for College

We will calculate a couple of extremes here. Let’s look at Temple in 1987-88 and Arkansas in 1993-94. Temple’s Coach John Chaney guided the 1987-88 Owls to the regular season number one ranking using an aggressive 2-3 matchup zone defense and a patient offense that valued every offensive possession like gold. Temple did not gamble on offense or defense, as they never attempted to force their offense or try to create turnovers with defensive pressure, preferring to force opponents to shoot poor shots.

Arkansas coach Nolan Richardson guided the Razorbacks to the national title in 1993-94. His teams pressed full court for 40 minutes (40 minutes of Hell) and played up-tempo fast-breaking offense. Arkansas committed more turnovers on offense, but they forced a lot more turnovers than average, and they came up with a lot of steals that led to easy points.

Temple in 1987-88 in 34 games
Offense: 305 Turnovers, 2,050 FGA, 704 FTA
Defense: 423 Turnovers, 1981 FGA, 513 FTA

Offensive TO Rate: 305/(2,050+(.465*704)+305) = .114 or 11.4%
Defensive TO Rate: 423/(1981+(.465*513)+423) = .160 or 16.0%

Arkansas in 1993-94 in 34 games
Offense: 539 Turnovers, 2,363 FGA, 834 FTA
Defense: 725 Turnovers, 2,234 FGA, 817 FTA

Offensive TO Rate: 539/(2,363+(.465*834)+539) = .164 or 16.4%
Defensive TO Rate: 725/(2,234+(.465*817)+725) = .217 or 21.7%

Which team was better at total turnover differential, Temple in 1988 or Arkansas in 1994? It was basically a wash. Temple played conservative basketball about as good as it could be played, going 32-2 and outscoring opponents by 15+ points per game. Arkansas played havoc basketball and went 31-3 outscoring opponents by almost 18 points per game. Both styles worked.

3. Offensive Rebound Rate (and, of course, Defensive Rebound Rate)
This measures the rate a team gets offensive rebounds and the rate in which it limits its opponents from getting offensive rebounds, which is obviously the rate of getting defensive rebounds. These stats allow the statistician to quickly see the opposite without having to perform double calculation. If Michigan State gets 36% of the rebounds on their offensive side of the floor, then Michigan State’s opponents will obviously get 64% of the rebounds on their defensive end of the floor.

The calculation for Offensive Rebound Rate is: Off. Reb/(Off. Reb + opponents Def. Reb),

 and thus the Defensive Rebound Rate is: Def. Reb/(Def. Reb + opponents Off. Reb)

Coach Tom Izzo has his Michigan State Spartans totally dominating the glass this year. Their rebounding margin of 11 boards per game is giving the Spartans an incredible advantage in games (how much we will see later).

Let’s calculate their Offensive Rebound Rate so far this season:
Offensive Rebounds = 190 Defensive Rebounds = 337
Opponents Offensive Rebounds = 176 Defensive Rebounds = 513

Michigan State’s Off. Rebound Rate = 190/(190+337) = .361 or 36.1%
Michigan State’s Def. Rebound Rate = 513/(513+176) = .748 or 74.8%

You can also figure total Rebound Rate, which isn’t a Four Factor, but easy enough by taking Michigan State’s percentage of total rebounds. (190+513)/(190+513+337+176) = 57.8%

4. Free Throw Rate
This is the most controversial of the Four Factors, and there are now multiple theories about how best to calculate this stat. The original formula was simply FTA/FGA. Many metric specialists (including me) believe this is not the best way to calculate free throw rate. For one, this original formula does not calculate made free throws. Shaquille O’Neal would be just as effective and maybe more effective than Steph Curry, and there is no way you can convince me that Shaq’s free throw rate should be as strong or stronger than Curry’s.
There is another school of thought, which is the one the PiRate Ratings have adopted, and that is Free Throws Made per 100 possessions. The calculation is a bit more involved since you need the number of possessions, but total possessions is now kept as a stat in college basketball, and there is a formula that accurately approximates possessions.

Our Accepted FT Rate Calculation is: FT Made per 100 possessions.

If you do not have the number of possessions, you calculate it this way:

NBA: FGA+ (.44 * FTA) – Off. Rebounds + Turnovers
College: FGA +(.465 * FTA – Off. Rebounds + Turnovers

An example from a real game–last Sunday’s Michigan vs. Indiana game.
Michigan took 58 shots in the game. They had 16 Free Throw Attemps, 7 offensive rebounds, and an amazing 2 turnovers.

Let’s calculate their possessions: 58 + (.465*16) -7 + 2 = 60.44

In the actual game box score, Michigan had 60 possessions. In other words, this formula is very accurate, and when there is a difference of one possession in the calculation, it usually is because the team that controlled the opening tap also had the last possession of the half.
Michigan made 12 free throws in their 60 possessions, so we now have to normalize this to how many they would have made in 100 possessions, which is quite simple.

12/60*100 = 20.0, so Michigan’s Free Throw Rate in this game was 20.0.

If we use the original formula, Michigan had 16 FTA and 58 FGA for a rate of 16/58 or 27.6%. We feel that this overstates Michigan’s rate here. Because there were just 60 possessions in this game (about as low as a 30-second shot clock game can produce), the rate was inflated.

There is a third school of thought by stating this formula as FT Made / FG Attempted, which is a bit more accurate than FTA/FGA, but we still prefer making our rate per 100 possessions.

Putting it all together
So, now you have the four factors. How can we take this data before a game is played and determine an estimated point spread? It is not an exact science.

Let’s return briefly to baseball. In baseball, you have the infamous WAR stat, where players are rated in wins above a replacement player, a replacement player being somebody you can pick up on waivers or call up from AAA. There is no WAR stat at this time for basketball, although many statisticians have tried to calculate one from game stats. The problem is that it is hard to judge defense in basketball compared to judging pitching and fielding in baseball.

So, the answer is to find a way to determine how much weight to place on each of the Four (Eight) Factors to try to determine which team is better.

In the NBA, this calculation is considerably easier than in college, because strength of schedule only marginally differs in pro basketball, as most teams play an equal schedule strength. It can be argued that Golden State’s schedule is easier than Philadelphia’s schedule, because the Warrior won’t play Golden State, while the 76ers don’t benefit from playing Philadelphia, but that becomes negligible as the season progresses.

In college basketball, the Patriot League and the Big Ten are not close to comparable, so Lehigh’s Four Factors’ stats are not equal with Michigan’s Four Factors’ stats.
Originally, Oliver determined that Effective Field Goal Percentage was by far the most important of the Four Factors, and since there are a lot more shots taken in a basketball game than anything else, it goes without saying that this factor should be the most important. If your team can consistently beat its opponents in eFG%, they will win more games than they lose. If your team has an eFG% that is 10% better than the opponents, then your team is playing at a championship level.

Oliver believed that eFG% was about 40% of the success or failure of a team. He stated that turnover rate was worth 25%, offensive rebound rate was worth 20%, and FT Rate was worth the remaining 15%. In back-testing, these numbers approximated success or failure in the NBA.

It took many hours of algorithm testing for the PiRate Ratings to come up with percentages to apply to these factors. In the end, we had to create two more factors to approach legitimate accuracy.
If you have followed this site during basketball season for some time, you have probably heard about our own creation called “R+T Factor.” This is a refined version of the rebounding rate and turnover rate, which probably is the reason why Oliver gave a bit more weight to turnovers than rebounds. The key is to separate turnovers into steals and everything else. A steal in basketball is worth more than a rebound. When a team steals the ball, the chances of getting an easy basket and/or drawing a foul is much higher than obtaining a rebound. After working with the formula for a few years, we finally came up with one we like.

Our R+T rating is: (R*2) + (S*.5) + (6-Opp S) + T, where
R= Rebound Margin
S= Average Steals Per Game
T= Turnover Margin

In 2017, one NCAA Team had a rebound margin of 12.3 per game.  They had a turnover margin of 1.8 per game (which means that they committed 1.8 fewer turnovers per game than their opponents), averaged 7.1 steals per game, and opponents averaged 6.2 steals per game.

This team’s R+T Rating was: (12.3*2) + (7.1*0.5) + (6-6.2) + 1.8 = 17.5

This team played in one of the top power conferences in the NCAA, and their rating of 17.5 was the best among the power conference teams.  When a power conference team has an R+T rating over 10, they are Sweet 16 caliber.  At 15, they are Final Four caliber.  So, it can be deduced that this team did fairly well in the 2017 tournament.

This team was national champion North Carolina.

This R+T stat tries to estimate the number of extra scoring opportunities a team gets in a game. The stat is much more valuable in the NCAA Tournament where there are 25-30 really strong teams playing. When the pressure is on, many times these extra opportunities decide the outcomes. While effective field goal percentage is still the number one variable, the R+T rating becomes more and more valuable as the tournament progresses. By the Sweet 16, the teams with the best R+T rating usually continue to advance, and in many years, the team with the number one R+T rating weighted by schedule strength wins the National Championship. In every season in the 21st Century, the champion has been among the nation’s leaders in R+T factor weighted against schedule strength.
The obvious second added factor in predicting basketball games is schedule strength. If a team in the Ivy League outscores its opposition by 10 points per game, they are not as good as a team from the ACC outscoring opponents by 10 points per game.

At the start of conference play, one SEC team may have played a non-conference schedule that on average is 10 points weaker per game than another team. Kentucky usually plays a much harder pre-conference schedule than Vanderbilt or Ole Miss. Tennessee has played a more difficult schedule than Missouri.

Once conferences have played more than half of their league schedules, you can even calculate ratings based only on conference games played and then take those ratings and rank the conferences overall to get a more accurate rating for every team.

For example, let’s say that on February 20 with 80% of the Big 12 conference games in the books, Texas Tech is 1 point better than Kansas, 3 points better than Iowa State, and so on down to Oklahoma State being 14 points weaker than Texas Tech. Let’s say that Stephen F. Austin is 3 points better than Abilene Christian in the Southland Conference and 5 points better than Sam Houston. Overall, the Big 12 is calculated to be 17 points better on average than the Southland conference, so Texas Tech would be 17 points better than SF Austin, 20 points better than Abilene Christian, and 22 points better than Sam Houston. Oklahoma State would then be 8 points better than Sam Houston, since they are 14 points weaker than Texas Tech.
We don’t actually figure the ratings this way, but we have an algorithm that does a similar calculation for every team based on their overall strength of schedule for the season. It is a close cousin but goes more in-depth than the Quadrant system in place by the NCAA Selection Committee and used by our Bracketology experts when they pick their weekly selections, which by the way you can now see our PiRate Rating Bracketology at the Bracket Matrix, at http://www.bracketmatrix.com/ Our abbreviation there is “Pi.”

There are many additional advanced analytical basketball ratings. Also, you can break down the individual ratings for all of the Four Factors, as well as ratings that calculate individual offensive and defensive efficiency and the Usage Rate, which tries to estimate how much a player is used in his team’s games by looking at what he does while he is in the game. Some teams most efficient players may not have the top usage rates on their teams, while less efficient players get more game usage. Teams can look at these stats and good coaches can adjust their lineups to get their more efficient players more game time, while limiting players that may be harming the team. Then, there are coaches that continue to play the wrong players for too many minutes, while their actual more efficient players don’t play enough. There is a phrase for these coaches that continually do this: We call it “Soon to be unemployed.”

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February 13, 2015

College Basketball and The Shot Clock

Imagine that you just purchased a very special smart phone from Honest Abe’s Electronics.  In point of fact, Honest Abe’s is located in the outer reaches of the Twilight Zone.

You attempt to text your special girl that you are on your way to meet her to go to the football game, but when you hit the “send” button, a flash of white light envelopes your body, and you are temporarily unable to see your surroundings.

Then, as if a flash of the camera has passed, you find that you have been transported to a parallel universe almost identical to the Earth, but with one difference.  You have been dropped in a 50-yard line seat at what appears to be a college football stadium you do not recognize.  A game program is in your hand telling you that you are at Tech Stadium ready to watch Tech play State.

As you read, you discover that both teams are 9-0.  The winner will advance to the Asteroid Bowl to face the tough Tigers team that is also 9-0 with one game to play.

“Great!” you think to yourself, and things couldn’t get any better when the college coeds sitting adjacent to you look like clones of Hannah Davis and Kate Upton, except their attire is a little outdated.  If you didn’t know any better, you would swear with those sweaters and bobby socks, they are trying to look like coeds from 1950’s America.

Somehow, you find a way to focus your attention on the football field.  The game kicks off at the 40-yard line, and the kicker punches it straight through with a steel-toed kicking shoe, much like was used in the 1950’s in America.

The kick sails 50 yards to the 10-yard line, and it is returned 18 yards to the 28, where State begins the first drive of the game.

Quickly, you cannot believe your eyes when Tech’s defense sure looks like the Wide Tackle 6 formation; you remember that your grandfather told you all about how he had played defensive guard.  As you chuckle quietly, you almost choke when State comes to the line in the Split-T formation.  On the first play, the State QB slides down the line and hands off to the right halfback on a straight-hitting dive play that picks up two yards.

After getting eight yards in three antiquated running plays, State punts, and Tech returns the ball to their 38 yard line.  Then, you notice something funny.  No substitutions were made in any of these plays since the kickoff.  Even the Tech kicker stayed in the game as a defensive halfback, if that’s what they called the position before there were cornerbacks.

Quickly, you realize that this parallel universe is a type of “Pleasantville.”  The 1950’s never ended, and for a second as you glance at your two new friends sitting either side of you, you realize something.  College football in the 1950’s may have sounded incredible when Gramps told you about the big games, but compared to today’s brand of football, it was as boring as watching the paint dry on the picket fence.  Thank goodness the NCAA made several rules’ changes between 1955 and 2014.

Eventually, Tech scores a touchdown to win the game 6-0, as the kicker shanked the point after.  The game ends, and you cannot wait to get out and look for the Marilyn Monroe and Kim Novak lookalikes that must exist in this place.

As you leave the stadium, a paper flies out of the wooden press box above.  It is a page of the stats for the game.

There were 120 total plays from scrimmage, of which 108 were running plays and 12 were passing plays.  The teams combined to complete five of the 12 pass attempts for 60 total yards through the air.  The 108 rushing attempts led to 350 rushing yards.  Tech won the game by holding onto the ball for the last eight minutes in a long drive that went from their 15 yard line to the State 30.

You notice that even though there were three opportunities for State to attempt field goals of 20-30 yards, the State coach never considered it.  Because there are limited substitutions in this brand of 1950’s college football, kicking specialists do not exist.  The State kicker is none other than one of the inside linebacker/offensive guards.

As you wish you were back in the 21st Century watching college football with 160 scrimmage plays, 80-100 passing plays, and more than 1,000 yards of offense, the white light comes from out of nowhere, and you are holding onto the hand of your girl, as you enter a 100,000-seat stadium to watch a game that could decide whether your favorite team will stay in the hunt for a college playoff spot.

This sounds impossible, correct?  Of course, it is, since Rod Serling is no longer around.  However, if you are a college basketball fan, you have been transported back to the equivalent of college football in the 1950’s, even if you didn’t see the white flash.

Yes, college basketball in 2015 is your parallel universe where all the exciting action has been taken out of the game.  Like the drastic change in total possessions between college football in 1954 and 2014, basketball has gone the opposite way with about 25 fewer possessions per game than 40-50 years ago.  And, the game has suffered immensely.

The average college basketball team today plays at a pace of 65 possessions per game.  Let’s take a look at the real past.  The statistics I am about to give you are not 100% exact, because certain data does not exist that can be used to make the data 100% accurate.  However, we can obtain a close approximation to possessions per game by looking at the statistics we do have.

In case you do not know, you can estimate college basketball possessions with great accuracy by using this formula:

FGA + (.465 * FTA) + TO – OR

Where FGA = field goal attempts, FTA = free throw attempts, TO = turnovers, and OR = offensive rebounds.

For example, if a team averages 52 field goal attempts, 22 free throw attempts, 13 turnovers, and 10 offensive rebounds per game, you can estimate their possessions per game by performing the easy math.

52 + (.465 * 22) + 13 – 10 = 65 possessions (rounded to the nearest whole number), which is about what the average is today in college basketball.

Many of you reading this know that at one time, I missed fewer than a half-dozen Vanderbilt University home basketball games between December 1963 and March of 2001.  It took 6 inches of snow and ice or a fever of 102 or more to keep me away.  Only a 2001 relocation to Colorado ended the streak.  When we returned to Nashville in time for the 2003-04 season, we did not buy tickets, as it was apparent that Vanderbilt would commence using the Princeton offense and its insomnia-curing style of play.  This style of play continued for a few years, but even when the Commodores switched offenses, the game as a whole had become too dull to warrant spending the money and time to attend the games.

The period between 1963-64 and 1975-76 were incredible for a Commodore season ticket holder, as Memorial Gymnasium was an even bigger 6th man for the home team than Cameron Indoor Stadium has been for Duke in the last 30 years.

Coach Roy Skinner did not believe in slow-paced basketball.  Reared in Kentucky, he believed in the principles of Adolph Rupp, and he produced basketball teams that lent themselves to sellouts.  The gym sold out for the season before Thanksgiving, in a time when the first games of the season were not played until the first Monday in December.

Two remodels brought the capacity of Memorial Gym to 15,626, and through the first half of the 1970’s, Vandy’s actual attendance at most games surpassed that amount.  More than one time, the city’s fire marshall, a VU fan himself, had to clear the aisles when those without a seat but with a ticket (often a student) tried to stake a claim and create a dangerous situation.

Why was Memorial Gym so packed, and why did Vanderbilt routinely win 90% of its home games in those days?  There are multiple reasons.  First, Vanderbilt was a perennial national power in the Skinner days.  In 17 seasons, his Vanderbilt squad only once finished with a losing record (still that 12-14 team defeated a 16-0 Kentucky team), and they finished with a losing SEC conference record just twice (6-8 and 8-10).  Skinner retired when his final team finished 12-6 in the SEC, which was considered a major disappointment.

The other reason for the sellouts, which is much more valid, is that Vanderbilt was one of 20-30 college teams that played up-tempo ball for 40 minutes every game.  80-point games were considered subpar performances.  It was routine to go to Memorial Gym and see the Commodores beat a name team 95-85.  Skinner did not schedule low and mid major opponents.  No, he routinely scheduled top 20 teams like North Carolina, Duke, Davidson, (when Davidson was an elite school similar to Gonzaga today), Kansas, St, John’s, Illinois, and SMU (when SMU was the Kentucky of the old Southwest Conference).

A typical game under a Skinner-coached Vanderbilt team found the Commodores with a stat line that looked like this:

FGA = 75, FTA = 30, TO = 18, OR = 16

Do the math, and you come to 91 possessions per game.  This is not just a typical stat line for one game; this is typical of an entire season.

In some games, like against Kentucky, North Carolina, or LSU, the number of possessions exceeded 100.  One night, I watched the Commodores approach 120 possessions in a game against Ole Miss (Vanderbilt scored 130 that night).

The average would be brought down because Vandy had three conference opponents that notoriously slowed the game down in most years.  Auburn used the shuffle offense and frequently held the ball for 45 seconds to a minute before shooting.  Remember, there was no shot clock in those days.

Until Ken Rosemond recruited beefy Bob Lienhard to Athens, Georgia also held the ball against teams like Vandy and Kentucky.  They outright stalled.

By far, the number one enemy of Vanderbilt fans was Tennessee coach Ray Mears.  Prior to the days where he recruited Ernie Grunfeld and Bernard King to Knoxville, Mears was a proponent of deliberate offense and a 1-3-1 trapping zone defense that led to “snoozeball,” for all but the orange-clad fans.

Take away the six games per year against Auburn, Georgia, and Tennessee, and Vanderbilt averaged about 100 possessions per game and 90 points per game.  This was without a shot clock or three-point shot.  Because the Commodores had outstanding guards that could shoot from 20 feet out, it is possible that 8-10 of their made shots per game would count as three-pointers today.  Add the shot clock into the equation, and you are looking at a program that would have averaged 100 points per game had the three slow-paced teams been forced to play with a shot clock.

Now, let’s look at a typical Tennessee team under Mears before the Ernie and Bernie show matriculated from the Empire State.  I will use the 1968-69 team, because I have their stats, and I have become a sort of friend with one of the players on that team, who lives just a jump shot away from me today.

That boring Volunteer team finished second in the SEC with a 13-5 conference record and 21-7 record overall, finishing third in the NIT, which in those days meant you were a top 20 team.

That Vol squad had a scoring margin of 67-58 in a college basketball environment when about 150 points per game was an average total.  This means the average total score in a UT game was about 17 percent below the national average, and about 33% below the average score of a Vanderbilt game that season.

Tennessee’s average possessions can be estimated thusly:

(56 FGA + (.465 * 20 FTA) + 14 TO – 11 OR = 68 possessions.

Remember, these stats came in a year with no shot clock, so teams could hold onto the ball for more than 35 seconds, even a minute if they could hold onto the ball.

Teams like Tennessee and their slow-paced style of play angered fans and coaches of other teams to the point where dozens of coaches and sportswriters, and thousands of fans clamored for a shot-clock.  Yes, those 68 possessions per game were a travesty then, as fans felt like they did not receive their money’s worth.  For what it’s worth, a college basketball ticket in 1968-69 at Memorial Gym went for $8, which had risen from $6 to help pay off the bill for the recent gymnasium expansion.  Today, 68 possessions in a game is above-average!

Put a 1968-69 college basketball fan in that Twilight Zone and transport him to the present day college basketball environment, and he will feel like you felt when you were taken to the parallel universe to watch that 6-0 football game.

Today’s college basketball with its 65 possessions per team per game pales in comparison to the brand of basketball played in the 1960’s and 1970’s when an average team played at a 80-90 possession per game pace.

The basketball purist believes that the rules should not be tinkered with, but I will counter that by saying that college basketball rules have continually been tinkered with through the decades, so basketball purity demands rules changes when they are needed.

The three-point shot and shot clock took basketball to new heights when they were instituted in the 1980’s, as in the early part of that decade, the game became stagnant with low-scoring games and some important games ending with the winning team not even scoring 40 points.

The shot-clock started at 45 seconds before moving to 35 seconds like it is today.  There is talk about trying a 30-second clock in this year’s NIT.  A few basketball experts support the 24-second clock like the NBA.

If you know me, you know I am a baseball sabermetrician.  I am into sports metrics and participate actively in sabermetric endeavors.

I can bore you with a lengthy treatise to show you exactly when a baseball manager should call for a sacrifice bunt attempt and when he should not.  I can tell you mathematically how to determine the efficiency a base stealer must have in order to help his team by trying to steal a base in every possible situation.

For basketball, I can also show you what changing the shot clock from 35 to 30 and to 24 seconds would do to total possessions per game and then make an assumption or two to refine what the math shows us.

In recent weeks, I have looked at tapes of numerous college games.  I had to take stimulants to stay awake through these boring dribblethons that led to teams getting anywhere from 52 to 69 possessions.  I tried to limit my monitoring to Top 20 teams, so I watched Kentucky, Duke, Virginia, Northern Iowa, Wisconsin, and others.

What I was looking for was the percentage of possessions where a shot was taken with five seconds or less on the shot clock.  Obviously, if the shot clock were reduced to 30 seconds per possession rather than 35, then these would be the possessions affected the most (there would be a secondary adjustment that I will not bore you with).

I found over the course of about 200 total games that on average in 2015, a college team will shoot the ball, turn the ball over, or draw a foul in the final five seconds of the shot clock about 18% of the time.  If we postulate that these 12 possessions per team per game now took exactly five fewer seconds due to the shot clock moving from 35 to 30 seconds, then you can estimate that the total number of possessions per team per game would rise slightly from 65 to 71 possessions per game.  This would represent merely a modest gain of 9% additional possessions.

What if we went all the way and tried a 24-second clock?  I have not had the opportunity to look at enough games to establish a pattern, but from the three dozen games I have charted this year, about 69% of all possessions exceed 24 seconds.  This includes offensive rebounds with immediate shots, turnovers, and fouls before 24 seconds elapsed, meaning that almost all other possessions used more than 24 seconds.

This would definitely change the game.  If you postulate that all the current possessions in excess of 24 seconds all of a sudden took a maximum of 24 seconds, then the number of total possessions per team per game would head north almost back to where it was in the 1970’s, when college basketball was definitely much more exciting to watch than it is today.

College football is up-tempo, and it is just behind the NFL in popularity.  College basketball is not there.  A 24-second clock would bring the excitement back, as teams would not be able to walk the ball up the floor and then dribble around the perimeter for 30 seconds.  It would be a team game once again with much less dribbling and much more passing and movement of players.  Time would not allow such stagnation as we see in today’s basketball game, where the players without the ball should be forced to purchase a ticket to enter the arena.

Let me address one additional item.  I have heard uninformed basketball fans make the claim that a 24-second clock would put an end to upsets and teams like Butler making deep runs in the NCAA Tournament and would leave teams like Kentucky and Duke in control of the sport.

This is bogus.  First, let’s look at Kentucky today.  The Wildcats average just 63 possessions per game, and they are dominating.  It is my belief, as well as the belief of others with higher basketball intelligence that if they are to be defeated this season, it will come from a team that speeds up the tempo and forces the Cats into enough turnovers to overcome the dominant rebounding the Blue Mist has.

Mathematically, in a game with limited possessions, there will be a lower standard deviation of points scored per possession.  The dominant team actually has a better mathematical chance of winning over the lesser-talented team.  In a game with higher possessions, the standard deviation of points scored per possession rises as well.  Definitely, there is a chance for a larger blowout win by the superior team, but there is also a greater chance that the dominant team will be off enough to fall to the opponent.

The up-tempo game may allow a Kentucky to beat an Auburn by 45 points rather than 10-15, but in the low-possession game, Kentucky may have a 97% chance of winning, while in the high-possession game, they may only have a 90% chance of winning.

What’s that?  Did I hear you asking me if a regular season college basketball game has ever been played using a 24-second clock?  The answer to that is, “Yes!”

There has been one regular season college game played with a 24-second clock, unfortunately more than 50 years ago. And, where was this college game played using said 24-second shot clock?  At none other than Memorial Gymnasium at Vanderbilt University under Coach Roy Skinner, Vanderbilt played Baylor in March of 1959 using an experimental 24-second clock.  The Bears led by double digits with less than 10 minutes to play, and in those days, a lead like this would have been nearly impossible to overcome in the time remaining.  However, with BU limited to just 24-seconds per possession, they could not freeze the ball.  Vanderbilt came back and won by a point on a jump shot from the top of the key in the closing seconds.

Imagine a college game where the teams cannot afford to dribble walk the ball up the floor for nine seconds.  Imagine teams unable to walk the ball up the floor and then dribble around the perimeter for a combined 25 seconds.  Imagine more teams utilizing full-court pressure to force opponents into using up 1/3 of a 24-second shot clock.  This will lead to basketball with 80-100 possessions once again.  With the three-point shot and 90 possessions per team per game, many teams will approach 100 points per game, and the truly great defensive teams will be great because they will score off their defense and force teams into .75 points per possession.

Individually, you will see a lot more double-doubles and even more triple doubles.  If a player averages 16 points and 8 rebounds today in a 65-possession environment, then he should produce close to 22 points and 11 rebounds per game in a 90-possession environment.

Back to Kentucky of 2015: the Wildcats are undefeated, but they are not in the same level of superiority as the UCLA teams of the 1960’s and 1970’s.  This team has liabilities that can be exploited by other teams.  We believe UK will not win the national championship this year if the right team shows up in their bracket.

What type of team can topple Kentucky in the Big Dance?  It will be a team that can run up and down the floor and score points before Karl-Anthony Towns, Willie Cauley-Stein, and Dakari Johnson can get there to alter the shots.  It will be a team that can run up and down the floor possession after possession to wear down the Cats’ big men, who have not yet been forced to play extended minutes at an accelerated pace. It will be the team that defensively can get in the passing lanes and steal passes and turn them into fast-break points.  We believe that the team that beats Kentucky will do so by forcing the tempo to a minimum of a 70-possession plus game.

Looking at some of the teams with good talent and an ability to play at a quicker pace, Iowa St., West Virginia, and North Carolina stand out as teams with enough talent to pull off a 70-possession pace against Kentucky.  Arizona and Duke could potentially play at that pace, but defensively neither can force Kentucky to speed up.

We do not believe that teams with paces similar to Kentucky can pull off the upset.  Virginia and Wisconsin would have to beat Kentucky by playing to the Wildcats’ strengths, and that does not look like a probable way to beat the Wildcats.

Speaking of the NCAA Tournament, be sure to return to this website Monday, February 16, after 1 PM Eastern Standard Time, to see our latest installment of our Terrific Two Dozen plus accurate bracketologists.  We bring together the most accurate bracketologists in the nation and form a composite master bracketology list to show you if your team needs to buy dancing shows or a new TV.  Forget the famous guys on the three and four-letter networks.  Our bracketologists historically fare much better in accuracy than the guys you may know.

Now, to the PiRate Ratings for this weekend’s top games.  Remember, these are first-year ratings, and we consider them to be experimental.  We use three separate algorithms incorporating basketball’s “four factors” and adjust the data for strength of schedule and home court advantage.  The PiRate Red and PiRate White are hitting close to 80% winners so far, while the PiRate Blue is lagging behind around 70%.  Unlike our football ratings, these ratings cannot be used to pick games against the spread, as they are set up only to pick the winner.  Yes, we supply a point-spread for each game, but the key part of this experimental rating is to try to work our way into picking a successful bracket come NCAA Tournament time.

Home Visitor Red White Blue
Saturday, February 14      
Kentucky South Carolina 23 19 16
Virginia Wake Forest 22 18 21
Gonzaga Pepperdine 24 20 15
Syracuse Duke -10 -6 -7
Butler Villanova -3 -1 2
Kansas Baylor 11 7 7
Louisville N. C. St. 16 12 11
Pittsburgh North Carolina -14 -7 -8
Iowa St. West Virginia 4 6 2
Illinois St. Wichita St. -10 -3 -10
Kansas St. Oklahoma -10 -8 -3
Penn St. Maryland -7 -1 -6
G W U V C U -5 -3 -2
T C U Oklahoma St. -6 -3 -5
Michigan St. Ohio St. -1 1 3
Ole Miss Arkansas 1 4 5
S M U Connecticut 8 6 10
Clemson Virginia Tech 13 8 9
Georgia Auburn 18 13 12
Georgia Tech Florida St. 10 7 2
Tennessee L S U -1 -1 -4
Missouri Mississippi St. -2 2 -5
Texas A&M Florida 2 3 7
Texas Texas Tech 19 16 21
Alabama Vanderbilt 2 4 12
Sunday, February 15
Wisconsin Illinois 19 15 13
Washington St. Arizona -28 -16 -17
Utah California 16 19 10
Missouri St. Northern Iowa -27 -11 -15
Northwestern Iowa -11 -6 -7
Purdue Nebraska 12 9 10
Boston College Miami (FL) -4 -1 3
Indiana Minnesota 3 5 8

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