To demonstrate how coaches can use ShotQ℠ as an evaluation tool today, we can evaluate a couple of AP Top 25 teams as an example.
 
AP Top 25 Teams – Potential Shooting Percentage Changes
 
Potential Negative eFG% Movement
Kansas defense - 23rd in eFG% and 121st in ShotQ℠
San Diego St. offense - 34th in eFG% and 158th in ShotQ℠
 
Stable Ranks
Michigan State defense - 9th in eFG% and 10th in ShotQ℠
Dayton offense - 1st in eFG% and 1st in ShotQ℠
 
Potential Positive eFG% Movement
Gonzaga defense - 145th in eFG% and 54th in ShotQ℠
Kentucky offense - 125th in eFG% and 18th in ShotQ℠
 
It is worth noting that just because we could expect a change in shooting percentage based off a team's ShotQ℠ doesn’t mean that the change will occur in the next game or games. Due to sample size limitations of the college basketball schedule, the change could take weeks, and, in a few cases, it may not occur in the remainder of the season.

As you are evaluating your team, it is not too late to improve your ShotQ℠ and likely your eFG% going forward. ShotQ℠ is not a static figure - it's dynamic. It accounts for every shot in each individual game. Therefore, teams can improve their ShotQ℠ throughout the season by taking better shots. Contact us for consultation services for ShotQ℠ among other tools and solutions.

Reallocating 1% of your total shots from 3s to midrange is a horribly inefficient trade. Threes, this year, are worth roughly 1.00 points per shot (PPS), while mid-range shots are worth around 0.70 PPS.
 
A trade of 3s for shots at the rim would’ve been valuable as those are worth 1.19 PPS. However, the percentage of shot attempts at the rim remains unchanged.
 
The further 3-point line has not only caused the 3-point percentage to decline, but due to the reallocation of shots, it is also sinking the 2-point percentage. Now that mid-range shots, the least efficient option, account for a larger share of 2s it has caused a 1.1% decrease in 2P% vs. last year.
 
What are a coach’s options now that the 3-point line has changed?
 
The easy answer based off points per shot data is that coaches should reallocate those 3s to shots at the rim instead of mid-range shots.
 
Many analysts claimed that the further 3-point line would provide better looks at the rim, but the shooting percentage at the rim is identical to last year. Despite it remaining equal to last year, shots at the rim are still the most valuable option.
 
However, a change is not necessary. It appears, at least for this year, the option may not be between a shot at the rim and a jumper; rather it’s between a jumper and a jumper. It is still better to take the new further 3s as they are worth 0.30 PPS more than mid-range attempts.
 
Coaches should use the same shot allocation strategy as last year and stop overreacting to this change.

​However, ShotQ is not a static figure - it's dynamic. It takes into account every shot for each individual game. Therefore, a team can improve their ShotQ throughout the season by taking better shots. The chart below shows how Texas improved from 205th to 98th in ShotQ. This 107-spot improvement corresponded with a 112-spot drop in their eFG% rank.

​These charts demonstrate why coaches should be tracking their ShotQ rank throughout the season instead of their eFG%.
 
We can also gain defensive insights by applying a similar process to D ShotQ and defensive eFG%. It's important to value the right shots on both sides of the ball.
 
Only 12 teams finished the season ranked in the top 50 for both offensive and defensive ShotQ.

ShotQ is updated daily on this page.
 
Teams need to gear their whole program towards improving shot quality. Everything else in individual coaching platforms ties into it. The players teams recruit, the plays team run, the skills team practice, and the things coaches preach all revolve around shot quality. 

As demonstrated above, the compounding effect of increased free throw repetitions, both in practice and games, coincides with the lift in FT% as the season progresses.

Another approach to study this question is to look at a player’s free throw percentage by every 20 free throws taken during the season.
 
The chart below shows a similar finding, demonstrating that a player’s free throw percentage is highly correlated with the free throw attempt number. A player is much more likely to make their 100th free throw attempt of the season than they are their 5th attempt. 

​To ensure that this conclusion is not influenced by an uneven distribution of players (with poor free throw shooters taking less overall free throws), I split the data out by a player’s free throw shooting level.
 
Using season-long free throw percentages, I split the players into two groups – good free throw shooters and poor free throw shooters. The chart below shows the free throw percentage by every 20 free throws attempted for the two groups.

​Again, the conclusion holds – free throw percentage improves over the course of the season in conjunction with the increase in free throws practiced per week.
 
Practicing free throws matters.
 
Free throw percentage not only improves throughout the season, but also over the course of a player’s career. The chart below shows the free throw percentages by year for the last 5 seasons. 

This trend goes beyond college basketball.

The NBA also sees a lift in FT% over the course of the season. The chart below shows the NBA FT% by every 20 free throws attempted for six seasons.
​

​Similar to the results from college basketball, NBA players are more likely to make a free throw later in the season than they are earlier in the year. NBA players also improve as free throw shooters over the course of their career.
 
Note the valleys that occur to start every season. These valleys demonstrate the FT% drop-off that occurs over the summer due to lower number of free throws shot in the off-season.

So why are free throws often ignored in the off-season? Why do we not consider it something for players to work on in their off-season development plan?
 
Practicing free throws in the off-season will help players realize their inevitable gains earlier in-season.
 
Unlike other strategies that increase win percentage in basketball, a team doesn’t need more money to practice free throws. Teams can win more games simply by having their players practice free throws more frequently.
 
Every team is searching for ways to gain incremental wins at low cost and practicing free throws should be at the top of every team’s list.

​In the press release announcing these changes, the NCAA stated, “The 3-point shooting percentage of teams in the 2019 NIT was 33%, compared with their regular season average of 35.2%.”
 
Their statement suggests that the D1 average should see about a 2.2 percentage point drop-off in 3P%. However, the NCAA’s attempt to interpret the impact of this change on shooting rate is poorly founded.
 
Simply using the NIT tournaments data is not enough to understand the impact of a distance change over the course of a season. First, there is a sample size issue. There were only 31 games played in NIT in 2019, which doesn’t provide us with enough data to comfortably project next year’s three-point percentage.
 
Second, the comparison made between regular season and tournament shooting percentages is misleading. Teams play easier competition throughout the year than they do in tournaments, which causes tournament shooting percentages to be lower than regular season percentages.
 
Proof?
 
Look no further than the NCAA tournament. The chart below compares the shooting percentages of NCAA tournament teams in the regular season versus NCAA tournament games. 

​NCAA tournament teams shot 1.3 percentage points worse in NCAA tournament games than they did in the regular season.
 
The distance wasn’t changed. The competition changed.
 
The more logical and accurate way to measure this impact is to use shot distance data.
 
Using shot distance data from last season, DribbleHandoff analyzed the projected drop-off from moving back about 1'5".
 
This analysis showed that the D1 average decreased from 34.4% to 33.7%, a 0.7 percentage point change.
 
Examining shots attempted between 21 to 26 feet (which accounts for over 90% of threes), the largest drop-off in any 1-foot increment is 1.2 percentage points. Therefore, even using the most extreme difference puts us below the NCAA’s suggested 2.2 percentage point drop.

The question then becomes, how does this impact the value of a three-point shot in relation to the other shots?
 
To compare shot locations, it is best to use expected point value, which is calculated by simply multiplying the shooting percentage of a given area by the points the shot is worth. For example, last season, threes were made at a 34.4% rate and are worth 3 points, so the expected point value is 1.03.
 
The chart below uses data from the last five years to compare the expected point value for shots at the rim, midrange shots, and threes.

​Over the last five years, shots at the rim are the most valuable shot. Threes were only .15 expected points behind these rim shots, while midrange shots were drastically less valuable.
 
The estimated three-point shooting percentage given the new three-point line is roughly 33.7% based on last year’s data, which translates to an expected point value of 1.01. This is about a .03 expected point decrease versus the last five years.
 
The difference between shots at the rim and threes is now more pronounced as the expected point margin will move from .15 to about .18.
 
Threes are still a highly valuable shot at this percentage and therefore defenses will be forced to defend an even larger area, which will provide better spacing. This could lead to shots at the rim becoming even more efficient.
 
In the same press release, the NCAA stated the following as one of their motivations for the distance change:
 
“Slowing the trend of the 3-point shot becoming too prevalent in men’s college basketball by making the shot a bit more challenging, while at the same time keeping the shot an integral part of the game.”
 
If a team reduces their three-point attempt rate and instead takes more shots at the rim, especially given good spacing, then that is a valuable trade-off. However, many analysts have suggested that players should now develop their mid-range game and should prepare to shot fake from the perimeter and take a few dribbles in for a mid-range jumper.
 
This is the trap of the new rule change!
 
Teams shouldn’t trade threes for mid-range shots because of this rule change. Even with the slightly lower shooting percentage from three, these shots from beyond the arc are still worth .29 expected points more than mid-range shots.
 
Over the last five seasons, the college game has made great strides in reducing the percentage of shots that come from the mid-range area. 

​As I shared at the 2019 NABC Convention, I built a model which shows that a team wins one extra game, on average, for every five percentage points decreased in mid-range attempt rate.

​What should teams do with the 21-foot jump shots that they took last year when they were still worth three points? Reallocate them to either shots at the rim or a new three. Anything but using them to fund more mid-range shots.
 
There will be teams that are influenced by the way the NCAA presented this rule change in their press release.
 
Don’t be misled - increasing the mid-range attempt rate will only cause teams to be less efficient and win fewer games.

​However, looking at defensive data, the scatterplot below shows that a player’s defensive impact has little to no role in how many minutes per game he will play.

Furthermore, an analysis of the top offensive and defensive players provides an alarming insight after adjusting each player’s ratings for team, conference, and opponents.
 
The top 20% of players in offensive rating have, on average, equal net ratings to the top 20% of players in defensive rating. Despite being equally efficient overall, the top offensive players receive an average of 4 more minutes per game.
 
Coaches are undervaluing defensive abilities, and therefore, teams aren't playing their most efficient lineups. Coaches are just playing their most efficient offensive players, without consideration for their defensive value.
 
Undervaluing defense affects all aspects of the game. Teams who properly value defense not only are rewarded by an increased scoring margin over the course of the season, but also find additional value from a player acquisition perspective. Valuing defense appropriately should completely alter recruiting and the transfer market.
 
The relationship between playing time and these ratings suggests that coaches are either actively choosing to value offense over defense, or there is too much to consider defensively with very little data to assist in the judgment.
 
Box scores are almost entirely offensively-based dashboards. They track points, shooting percentages, assists, turnovers, number of shots, and offensive rebounds while offering just defensive rebounds, steals, and blocks on the defensive end.
 
Even looking at advanced metrics, the offense is still heavily favored in the number of metrics tracked.
 
Most coaches use these box score stats and their advanced versions, such as assist percentage, to inform their lineup and playing time decisions. These metrics help serve as a somewhat unconscious proxy to offensive rating, while the lack of metrics defensively provides little insight into defensive rating.
 
Offensive stats, whether using box score or advanced, explain about four times more of the change in offensive rating than the defensive metrics explain in defensive rating.
 
If anything should be overvalued, it should be defense.
 
Even though the top 20% of offensive players and top 20% of defensive players have equal adjusted net ratings, a top defensive player with limited offense is arguably more valuable than a top offensive player with limited defense. The data suggest that the offensively talented player would see more minutes, but strategically, these players can be exposed with different strategies.
 
Coaches have the ability to find ways for a sub-par offensive player to provide value. Whereas on defense, depending on the scheme, the offense can target and attack a weaker defender every trip down the floor.
 
For example, if a defense is switching, the offense can target the weaker defender by incorporating him in a ball screen or a dribble handoff.
 
On the other side of the ball, weaker offensive players can be used as screeners and can crash the offensive boards.
 
Regardless of the root cause, defense continues to be undervalued. The game is being managed at a sub-optimal level. This large-scale market inefficiency offers tremendous returns for teams willing to invest in analytically studying defensive possessions.

​Swapping Francis in for one of the two bigs provides Texas Tech with more spacing and shooting offensively and a more versatile or switchable player on defense.
 
Beyond his impact on the lineup, Francis’ value is clearly demonstrated by his individual on/off court data from Texas Tech’s conference season (excluding garbage time). With Francis on the court they had a +.18 net rating compared to a +.06 net rating with him off the court.

​As noted in the chart above, he greatly impacts 2-point attempts on the defensive end. With Francis on the court, opponents shot 10% worse on 2’s than with him off the court.
 
Texas Tech is better equipped to switch everything with Francis in for one of the two bigs. The 6’5” 215-pound Francis can defend a wider range of positions and skill sets than both the 6’8” 250-pound Odiase and 6’10” 205-pound Owens.
 
The first possession of the game versus Iowa State is a great example. The Cyclones attacked the 2 bigs in the starting lineup by involving both bigs in a ball screen, which caused a miscommunication on the switch and resulted in an open 3 for Marial Shayok.

Francis is key to Texas Tech’s success.
 
He even has a positive impact on his teammates’ performance. The graphic below shows the net rating for each player when they are on the court with Francis versus without him. All of their rotation players have a higher net rating when playing with Francis compared to when they play without him.

The Red Raiders will need Francis’ defense and ‘switchability’ to win a National Championship, especially against these remaining teams.
 
Texas Tech’s tremendous season has the potential to end in an NCAA Championship – and it all starts with unsung hero, defensive stalwart, and sixth man Brandone Francis.

​ 
As noted in the chart above, he really impacts 2-point attempts – both offensively and defensively. This is no surprise as Zion is shooting 75% on 2s (top 5 in the nation) and is one of college basketball’s top shot blockers on the other end of the floor.
 
In addition to his 2-point dominance, he provides an impact on offensive rebounding. Zion not only pulls boards down at the offensive end at a higher rate than his teammates, but he also does a better job of limiting their opponents. The chart below shows the offensive rebounding margin when a given player is on the court.
 

​With Zion on the court, Duke grabs 6 more offensive rebounds per 100 possessions than their opponents.
 
Beyond his influence on 2s and offensive rebounding, he improves his teammates’ performance.
 
The graphic below shows the net rating for each player when they are on the court with Zion and without him. All of their rotation players have a negative net rating without him and almost all have a positive net rating with him.

​No other player on the team has a net rating impact that resembles anything close to Zion’s positive impact.
 
If Zion stopped playing this season, then Duke could be in real trouble. Though they have 3 other top freshmen, they haven’t played well without Zion and their bench depth hasn’t developed.
 
The chart below shows the freshman combination of R.J. Barrett, Cam Reddish, and Tre Jones with and without Zion Williamson. 

Zion is the key to Duke’s success.

Their starting lineup, which consists of these 4 freshmen and Marques Bolden, is a super efficient lineup with a +.34 net rating. This is a championship contender-level lineup and ranks in the top 5% of lineup net ratings over the last few seasons.
 
The example of Zion is a great way to visualize all of the different insights that can be gained by analyzing on/off court data. This in-depth analysis remains underutilized in college basketball, while it is has become common in the NBA. As with many of the strategies DribbleHandoff identifies, the returns will be greater for the coaches who are the first movers.

​Over the last 5 seasons, these isolations continue to increase in the NBA while they have remained constant in college basketball.
 
College coaches should study the NBA and find ways to incorporate this into their offense against switching defenses.
 
This trend is rapidly accelerating in the NBA playoffs and really became noticeable last season.
 
The table below shows the percentage for both the NBA regular season and playoffs.

​It’s one thing to acknowledge that this strategy is quickly trending upward, but is it providing an analytical advantage?
 
To answer that, it’s best to compare apples to apples. Here, that means comparing the isolation possessions resulting from a switched ball screen versus the efficiency of the ball handler in the all other ball screen possessions.
 
In the NBA regular season, the isolation possessions from a switch are roughly .05 to .10 points per possession (PPP) more valuable than the ball screen ball handler possessions. In last year’s playoffs, this number was .13 PPP and its value is accelerating.
 
The table below shows the additional efficiency offered by the isolation in the playoffs over the last 3 years.

This is just the NBA average. If a team has an elite offensive player, the gains can be substantial. Look no farther than James Harden.
 
In the 2018 playoffs, Harden averaged almost 6.5 possessions per game from an isolation after a ball screen switch. This was roughly 2 possessions more than the next closest player – LeBron James.
 
It was also almost 2.5 possessions per game more than he averaged in the regular season. He wasn’t just a leader in the volume of these shots. He also had the highest efficiency among players with at least 1 possession per game in last year’s regular season.

​In the playoffs, the Rockets were even more committed to setting hard screens and forcing their man on to Harden. He then destroyed the mismatch at the rim and from 3:

If you expected some sort of regression to the mean or fall off this season, think again. Harden has actually been more efficient than he was last year.

​Players like Chris Paul, LeBron James, and Kevin Durant have also added this strategy to their repertoire.
 
Which players could effectively add this at the college level?
 
Many are capable, but two names stand out above the rest – Antoine Davis and Markus Howard.
 
The players that have done it the best at the NBA level are both elite shooters and able to break their man down. These are players that can make shots off the dribble.
 
The scatterplot below shows the usage and efficiency of players shooting off the dribble since 2013.

Davis’ attempts per game off the dribble are record-smashing: he’s averaging 3 more shots than the next closest player since 2013.
 
Both players have already proven to be super- efficient when isolating after a switched ball screen, but the attempts should drastically increase. Howard is averaging 0.8 ball possessions per game with an incredible efficiency of 1.6 PPP. Davis isn’t far behind averaging 0.4 possessions and 1.13 PPP.
 
The off the dribble shots below illustrates their potential dominance isolating against mismatches:
 
Antoine Davis:
​

Finding elite players to attack the mismatch after the switch is only part of the equation; identifying the right players to target on defense will only make these possessions more efficient.
 
Last year, NBA teams hunted for mismatches when they noticed that defenses were switching the screen, but it became even more prevalent in the playoffs.
 
No one was targeted in the playoffs more than Steph Curry. He was isolated after the switch 3.5 possessions per game, despite only being targeted 0.35 possessions per game in the regular season.
 
The table below shows the list of the 12 most targeted players in the playoffs ranked by their defensive efficiency. Despite only being targeted 1.7 possessions per game, Turner conceded the most points per possession.

​For coaches with one or more weak defenders that are likely to be targeted in switches, the best ball screen strategy remains over with a soft hedge to force the ball handler to take the midrange shot.
 
And as with many of the strategies I have written about the returns will be greater for the coaches that are the first movers.

The ball handler garners so much attention because they possess the ball, but the ball handler is the least efficient player involved in the ball screen, because the dribble jumper and runner hold the least points per shot. Most of the shots from these two options come from the same area – midrange and non-restricted area paint shots.
 
As I wrote in the last article, midrange attempts are terribly inefficient shots.
 
On all offensive actions, midrange shots are worth about .71 points per shot. The dribble jumper in this analysis includes jumpers from both 2 and 3, which is boosting the value of this shot in the analysis.
 
Therefore, teams should defend the ball screen by forcing the ball handler to take a dribble jumper from midrange or a runner.
 
The offense has already proven they will take it too. Runners and dribble jumpers by the ball handler account for 31% of the possessions using the ball screen.
 
What’s the best way to force the ball handler to take a midrange shot off the ball screen? There are a few different strategies that can be effective, but the best is probably an over with a soft hedge.
 
However, personnel, both offensively and defensively, should dictate how the defense guards the ball screen.
 
Offenses will remain committed to creating space using the ball screen. It’s going to be more than just a trend. But that doesn’t mean the defense is helpless. In the battle for space, defenses need to counter by offering some space - the inefficient midrange area for the ball handler.