# Wednesday’s Games

## Home |
## Visitor |
## Spread |

## Alabama |
## Texas A&M |
## 13.1 |

## American |
## Navy |
## 4.9 |

## Arkansas St. |
## UL Monroe |
## 7.0 |

## Bradley |
## Missouri St. |
## 4.9 |

## Cincinnati |
## Central Florida |
## 11.2 |

## Citadel |
## Chattanooga |
## -8.4 |

## Colgate |
## Lehigh |
## 15.3 |

## DePaul |
## Villanova |
## -3.4 |

## Drake |
## Valparaiso |
## 2.4 |

## Duquesne |
## George Washington |
## 11.4 |

## East Tennessee St. |
## Furman |
## 4.6 |

## Fresno St. |
## Air Force |
## 6.2 |

## Georgetown |
## Providence |
## 4.8 |

## Georgia |
## Auburn |
## -3.4 |

## Houston |
## Tulsa |
## 10.0 |

## Houston Baptist |
## Abilene Christian |
## -10.7 |

## Incarnate Word |
## McNeese |
## -7.3 |

## La Salle |
## Fordham |
## 7.6 |

## Lafayette |
## Boston U |
## -0.3 |

## Louisville |
## Syracuse |
## 10.0 |

## Loyola (Chi.) |
## Illinois St. |
## 11.4 |

## Loyola (MD) |
## Army |
## 2.6 |

## Memphis |
## East Carolina |
## 15.8 |

## Mercer |
## Samford |
## 7.5 |

## Minnesota |
## Indiana |
## 4.9 |

## Mississippi St. |
## South Carolina |
## 5.5 |

## North Carolina St. |
## Duke |
## -8.4 |

## North Dakota |
## South Dakota St. |
## -4.1 |

## Northwestern St. |
## Nicholls St. |
## -4.3 |

## Richmond |
## George Mason |
## 11.8 |

## Rutgers |
## Michigan |
## 1.2 |

## Sam Houston St. |
## Lamar |
## 8.8 |

## San Jose St. |
## Boise St. |
## -11.5 |

## Seton Hall |
## Butler |
## 5.7 |

## Siena |
## Iona |
## 7.0 |

## South Dakota |
## North Dakota St. |
## 0.3 |

## Stephen F. Austin |
## Central Arkansas |
## 14.0 |

## Texas |
## TCU |
## 4.1 |

## Texas Tech |
## Kansas St. |
## 12.0 |

## Tulane |
## SMU |
## -6.1 |

## UC Irvine |
## Long Beach St. |
## 15.7 |

## UMKC |
## California Baptist |
## -0.2 |

## UNC Greensboro |
## Wofford |
## 10.1 |

## Utah St. |
## Wyoming |
## 21.1 |

## Virginia |
## Boston College |
## 11.6 |

## Virginia Tech |
## Miami |
## 4.9 |

## Wake Forest |
## Georgia Tech |
## 1.3 |

## Washington St. |
## California |
## 6.6 |

## Western Carolina |
## VMI |
## 9.5 |

# Wednesday’s Key TV Games

## Time (EST) |
## Network |
## Home |
## Visitor |

## 6:30 PM |
## FS1 |
## Seton Hall |
## Butler |

## 7:00 PM |
## ESPN |
## Louisville |
## Syracuse |

## 7:00 PM |
## BTN |
## Rutgers |
## Michigan |

## 7:00 PM |
## ESPN+ |
## East Tennessee St. |
## Furman |

## 8:00 PM |
## ESPN3 |
## South Dakota |
## North Dakota St. |

## 8:30 PM |
## FS1 |
## Georgetown |
## Providence |

## 9:00 PM |
## ESPN |
## North Carolina St. |
## Duke |

## 9:00 PM |
## ESPNU |
## Houston |
## Tulsa |

## 9:00 PM |
## BTN |
## Minnesota |
## Indiana |

## 9:00 PM |
## SECN |
## Mississippi St. |
## South Carolina |

# In Order To Perform A More Perfect Metric

#### If you follow this website on a semi-regular basis, you know that our R+T Rating has been the one unique metric used by us when predicting NCAA Tournament favorites.

#### For those of you that are new to this site, our R+T Rating was created two decades ago to estimate the extra scoring opportunities (by points) each team might be better than average in the NCAA Tournament. We realized long ago that just like the “Money Ball” type of baseball strategies did not work well in the Major League Playoffs, the NCAA Tournament presented its own unique differences and required more than the Four Factors to determine winners when only the good to great teams remain.

#### The current formula for R+T consists of counting stats, but we have realized for some time that rate stats are much more accurate. Using baseball as an example, a counting stat would be Johnny Horsehide hitting 43 home runs and driving in 118 runs. These two stats might lead the Majors, but these stats may not reveal what we want them to reveal. Gary Goodeye might hit just 34 home runs and drive in 95 runs, but Good Ole Gary might be a better home run hitter than Johnny. How many times did Horsehide come to the plate? What if Horsehide walked 34 times in 702 plate appearances while playing for a team that had three all-stars hitting in front of him, all of whom have on-base percentages of .400 or better?

#### What if Goodeye had 650 plate appearances playing on a team that was quite weak offensively? Let’s say his teammates that batted in front of him had one-base percentages between .320 and .335. Let’s say that Goodeye didn’t always get good pitches when he appeared in the batter’s box, and he walked 125 times.

#### Now, if we look at the number of home runs hit per at bat or plate appearances that did not end in a walk (or hit by pitch or sacrifice), we will see that Goodeye actually hit home runs at a slightly better rate than Horsehide. As for runs batted in, that statistic is close to meaningless, because in order to drive runs in, runners must be on base. So, the RBI stat is more reliant on the other players on the team. It could be that Goodeye drove in runners better than Horsehide, because when we look at how many runners were on base and what base they were on, Goodeye might have had a better percentage at driving those runners in.

#### Back to basketball. A team with a rebounding advantage of 43-37 has a +6 margin. A team with a rebounding advantage of 35-30 has a +5 margin. Using counting stats, the 43-37 team is one better than the 35-30 team. But, the 35-30 team rebounded 53.85% of the missed shots, while the 43-37 team only rebounded 53.75% of the shots. So, the 35-30 team is a little better than the 43-37 team on the surface.

#### However, it is harder to get offensive rebounds than it is to get defensive rebounds. In fact, data throughout the calculated history of college basketball shows that an offensive rebound is worth better than 2 1/2 defensive rebounds. The Four Factors breaks rebounding rate down into offensive and defensive rates.

#### Let’s say that in a game, Team A shot 25 of 60 for 41.7% while shooting 16-22 at the foul line for 72.7%. Team B shot 28 of 58 for 48.3% while shooting 10-17 at the foul line for 58.8%. Team A hit one more three point basket than Team B and one by a point.

#### Now, let’s look at the rebounding for this game. First, there were five dead ball rebounds, which we do not count as actual rebounds. The statistical rules in basketball is that for every missed shot, there must be a rebound. When a player is at the foul line for two shots, and he misses the first shot, there is not a real rebound. The foul shooting team gets credited with a dead ball rebound.

#### To the contrary, team rebounds do count, because these are rebounds in which possession is determined. When a missed shot ends up out of bounds before possession can be guaranteed, the team that gets possession out of bounds receives an offensive rebound.

#### In this game after removing the five dead ball rebounds, there were 73 rebounds to be had. When Team A shot, there were 39 potential rebounds following misses, while when Team B shot, there were 34 potential rebounds following misses.

#### Looking at the stats, Team A finished with 13 offensive rebounds and 23 defensive rebounds for 36 total rebounds. Team B finished with 11 offensive rebounds and 26 defensive rebounds for 37 total rebounds.

#### Team B had a counting rebounding margin of +1, while Team A had a margin of -1. However, let’s now look at the percentage of offensive rebounds each team enjoyed. Team A had 13 offensive rebounds out of 39 missed shots, which is 33.3% of the missed shots at their offensive end. Team B had 11 offensive rebounds out of 34 missed shots for 32.4% of the missed shots at their offensive end.

#### Looking at the rate stats, Team A may have retrieved fewer total rebounds than Team B, but they were actually the better rebounding team in this game by almost 1%.

#### The rate data is obviously more telling than counting data, but how can we determine a point value to substitute rate data for counting data in our R+T Rating, which in the past has picked a lot of surprise NCAA Tournament winners?

#### We’ve been back-testing values daily for two months. We had to include a constant in our formulas to smooth out the results to make the numbers mean something. Without the constant, the results were too far apart to mean something. Tiny differences led to major spreads, and that did not tell us what we wanted.

#### After about 150 to 175 different attempts, we believe (HOPE) that we have finally had a breakthrough. The following formula will be explained after we reveal it:

## ((R*8)+(S*2+((5-Opp S)*2)+(T*4)))/2.75

#### This formula now refers to Rate Stats. The “R” in the formula now stands for Rebounding Rate. This is a combination of both offensive and defensive rebounding rate and it is a deviation from the norm and not just a percentage. The norm in our experiment is 28.1%. If a team has an offensive rebounding rate above this number, it is above average, and if it is below this number, it is below average. Thus, the norm for defensive rebounding rate is the opposite of the above number, or 71.9%. We then calculate our R part of the formula by taking each team’s offensive rate minus 28.1 plus their defensive rate minus 71.9 and then add the two results and divide by 2.

#### Example: Today, Houston has an offensive rebounding rate of 38.5%, which is 10.4% higher than average (we experimented with using the actual percentage better which would have been 37% better than average, but we never arrived at a usable final number doing so.) Houston’s defensive rebounding rate is 74.5%, which is 2.6% better than average.

#### We then take both numbers (+10.4 & +2.6), sum the numbers, and divide by 2 to get +6.5. That would be the R number for Houston in the new formula.

#### Let’s now update our formula:

## ((6.5*8)+(S*2+((5-Opp S)*2)+(T*4)))/2.75

#### The rest of this formula uses the same system as above. The norm for steals (S) is 9.2% for both offense and defense.

#### Houston has a 7.4% steal rate, which is 1.8% below average. Houston’s opponents have a 7.5% steal rate against them, which is 1.7% above average for Houston. Once again, we update the formula.

## ((6.5*8)+(-1.8*2+((5- [-1.7])*2)+(T*4)))/2.75

#### Now, we need Turnover rates, both offensive and defensive. The norm for turnover rate is 16.9%. Obviously, the lower the offensive turnover rate is, the better, and the higher the defensive turnover rate is, the better. Houston’s offensive turnover rate is 14.9%, which is 2.0% better than average. The Cougars’ defensive turnover rate is 15.8%, which is 1.1% below average. We sum the two numbers and divide by 2: 2.0 + (-1.1) = 0.9 and divided by 2 = 0.45. The 0.45 is now our T in the equation and we are ready to solve the equation. The 2.75 by the way is our constant that when used brings the results into what we hope is a usable formula.

## ((6.5*8)+(-1.8*2+((5- [-1.7])*2)+(.45*4)))/2.75

#### We will simplify the formula in case you have math anxiety like one of our PiRate lasses.

## ((52)+(-3.6+(6.7*2)+(1.8)))/2.75

## (52+9.8+1.8)/2.75

## 63.6/2.75 = 23.13

#### We will have some growing pains with this new formula, and there’s a good chance that the numbers will be tweaked in the future, but this is the Rate Version of the R+T Rating that we will use in the NCAA Tournament. Because it is an experiment, we will also use the original R+T formula when we issue our Bracketnomics 2020 edition.

#### Here are the two formulas together for you to compare.

# Original R+T using actual counting margins and averages

# (R * 2) + (S * .5) + (6 – Opp S) + T

# R = Rebounding Margin

S = Average Steals Per Game

T = Turnover Margin

# New Experimental R+T using rate the percentage number difference from the norm

# ((R*8)+(S*2+((5-Opp S)*2)+(T*4)))/2.75

# The 2020 Norms

# Offensive Rebounding: 28.1%

# Defensive Rebounding: 71.9%

# Steals (O&D): 9.2%

# Turnovers (O&D) 16.9%