#### Important Notes:

- Please note that this is meant to be the start of the discussion of using
advanced metrics to become better at basketball. This is NOT meant to be thedefinitive “last word”on the subject. It’s my hope that Philippine teamsembrace this as an opportunityto measure themselves using some of the advanced metrics used by the best in the world and achieve its dreams of becoming one of the best, in Asia or maybe even the world.- I used the stats available for the
2013 FIBA Asia tournament held in Manilaand I focused on the Philippine teams performance by using advanced analytics methods toreveal ways we can best use what we have, in order to choose the most efficient players for the FIBA Worlds.I hope that using these data driven methods will help increase our chances of success in International Basketball competitions. While it’s only a small sample size, this dataset isreally the only onewe have that reflects where we are now and what we have moving forward.- The methods I’ve used and describe here can also be used for
local professional, amateur, and college basketball teams looking for an extra edge. If any of the Coaches, Managers, or Owners want to discuss how to effectively incorporate advanced basketball analytics in their respective teams, do feel free to get in touch by following me on Twitter (@sarcastillo). It would be interesting to see what these methods reveal for your respective teams. I’m pretty confident your data will reveal how you can improve your chances at winning even more basketball games.

### The Dawn of Advanced Basketball Analytics

Analytics used to be just for baseball, which most people learned about first by reading (and maybe even watching) how Billy Beane constructed the Oakland Athletics, detailed in Michael Lewis’ book “Moneyball”. Basketball analytics are a different animal altogether though. Dan Rosenbaum, one of the early thought leaders on Basketball Analytics, said:

The

bar is higher for statistical analysis in basketballthan it is in baseball. Ultimately this will greatly benefit the teams that incorporate skilled statistical analysts in the right way, because the greater complexities in basketball will mean that itwill be harder for other teams to ever catch up with the first teams that get this right.(Emphasis mine)

Fact is, no one likes being judged *just by the numbers* (or being boiled down to just *A* number). In a sport as dynamic as basketball, where a lot of things don’t show up in the stat sheets, players would like to be judged by the only metric that matters- winning basketball games. So why do we need to use advanced analytics for basketball? Can these “advanced” analytics help you play better and win more games? Should we even try to use it to help make “Basketball Decisions”? Yes, and Yes:

The reason to use stats in any field is because humans are poor at evaluating probability. We tend to see patterns where there aren’t, overestimate the probability of low frequency events and, most importantly, have a tendency towards confirmation bias – looking for evidence that confirm our preexisting beliefs.

Stats can’t answer all of the questions but they can rule out some wrong answers that have intuitive appeal and focus attention on possibilities that are more likely to be correct.

– NickS at APBRmetrics

The thing is basketball teams, just like a successful business, need to **look beyond the raw metrics available to help improve its chances of winning**. Simply relying on “decisions”, especially those backed up only by the “eye test” and the normal raw numbers on a box score, can be **fool’s gold**. Truth be told, we always believe we are logical, analytic beings capable of making decisions without bias. But a lot of times decisions are made by choosing the path of least resistance or what we have become used to. This is what the book **“The Power of Habit: Why We Do What We Do In Life and Business”** by Charles Duhigg suggests:

One paper published by a Duke University researcher in 2006 found that more than 40 percent of the actions people performed each day weren’t actual decisions, but habits.”[1]

We humans like to think we’re always rational, making decisions everytime, when in fact we often use habits to decide what we want to do as it’s the shorter route to getting things done.

For example, when you brushed your teeth this morning

(you did brush, right :))did you start from the left or right side of your mouth? Did you brush up or down first? How many times did you brush each tooth, on average?If you’re like most people (save for those with OCD), than you will think of this the next time you brush cause you don’t really know the answer at the moment.

What does this have to do with basketball analytics, you may ask? Well, everything. Take this scenario: Player A, a gifted athlete who plays multiple positions for your team, scores the most points on the team, yet misses the most shots, and turns it over the most too (since the ball is probably mostly in his hands). Naturally, Player A will probably have a lion’s share of the minutes for the position you start him in. But is Player A playing the right way? His stats may look stellar on a traditional boxscore, but how do you get even more out of him? The box score measures raw data, **totals of effort on the court translated into numbers** we can reference. I say **these deserve a deeper look**. Why? Simply because you may be “deciding” to put certain players in and out of the rotation based on habit, which is further based on *data you’re used to* when making such “decisions”.

What if there’s

more to the data found in the boxscore than meets the eye?What if your best rebounder in the boxscores isn’t the most efficient rebounder on the team?

What if your best rebounder doesn’t corral enough Offensive rebounds? Then who really is your best rebounder, defensive and offensive, on average? What if your best rebounder is warming up the bench just when you need a crucial rebound to get an all important stop in the game?

No rebounds, no rings right?

Is your most efficient rebounder playing during the crucial times you need that board? The boxscore doesn’t give us this information on the surface. However, we can and *should* dig a little deeper. Knowing that not everything is captured in a boxscore, how do we dig deeper into the available data and learn what else we need to measure in order to translate this into wins? Here we’ll take a look at Basketball metrics that should matter to teams in the Philippines and we’ll learn how using advanced analytics methods, with the available data, can help successfully improve the chances of winning in Basketball. Welcome to the new and exciting world of advanced basketball analytics in the Philippines.

### Introduction to the Basketball Efficiency Quotient (BEQ)

This is my attempt at deriving a number to represent how efficient a basketball player is during the course of a game. Using some of basketball’s advanced statisticians work before me, such as Dean Oliver, Dan Rosenbaum, John Hollinger, Kevin Pelton, Neil Paine, EvanZ, and many more, I adjusted weights according to what is important, in my mind, to an efficient basketball player. Knowing the tendency of Hollinger’s PER to focus on the offensive side of the game, I will try to incorporate defensive effort. This will obviously require new metrics to be tracked in order to successfully gauge a player’s complete efficiency, which is the point of this entire exercise.

#### Purpose Of The Basketball Efficiency Quotient (BEQ)

To effectively capture on-court effort with the intent of going beyond the raw data of a traditional boxscore, I dug a little deeper to analyze what these numbers mean. Smarter people have come before me and I’m happy that they did. Using Dean Oliver’s Four Factors and computing for pace adjusted metrics based on the NBA games, plus closely studying the PER metrics by John Hollinger, I tried my best to approximate a basketball player’s efficiency. I call this a players **Basketball Efficiency Quotient (BEQ). **

A player’s BEQ is a number a player can work to improve and a coach can use to help his players focus on improving the right things. An intended effect of using the BEQ as one of the basketball metrics of choice is that your team will begin to try playing basketball the *“right way, making the smart basketball play”.* This simply means lessening forced shots (tightly contested shots from *just inside the 3 point line*, for example), increasing the likelihood of the extra pass being made, increasing awareness of the pass before the assist (which is almost as important as it sets up the entire offensive possession), defenders working hard to stop the penetration, players gung-ho about recovering 50–50 balls, etc.

The BEQ is an approximation of a players total, all-out effort (or non-effort) during the course of a game, both on defense and on offense. Players can impact a game without scoring the most, rebounding the most, or blocking the most shots. We’re looking for efficiency.

How it intends to do that is by giving players and coaches a “common metric” to measure efficiency on the basketball court. The BEQ is an approximation of a players total, all-out effort (or non-effort) during the course of a game, both on defense and on offense. Players can impact a game without scoring the most, rebounding the most, or blocking the most shots. We’re looking for efficiency. Setting the perfect pick, grabbing the most of available rebounds when on the court, timely closeouts to defend the three pointer, taking a charge, forcing turnovers by stealing or deflections, changing shots in the paint by being aware of where the shot is coming from and simply “going straight up” are some examples of how you can impact the outcome of a basketball game. These also rarely show up on a normal boxscore. Some of these can be derived from boxscores though. My hope is by using advanced basketball metrics, we can help players form the right habits and instincts on the court. If they can execute an offensive and defensive plan without thinking, as it’s become a habit from practicing and focusing on the small things that lead to success, it’s a gain for the player and for the team.

#### Limitations of BEQ

I’d like to discuss some of the limitations of what I’m trying to propose here. Basketball, in all its complexity, can *never be just about the numbers*. The boxscore can easily be manipulated and mistakes can and have been made in the past.

When is an assist actually an assist? If the player receiving the pass isn’t directly going to the basket, dribbles, and makes a move before making the shot, is that

still an assist?

The old adage “Garbage In, Garbage Out” can easily poison this analysis since we are, in fact, **using the boxscore as a data source**. While I do propose tracking additional stats (later on as you’ll discover) which seek to measure defense a little more accurately, truth be told , this is an entirely new field for Philippine Basketball and we are still discovering how far statistical analysis can really go in helping teams gain an edge. Also, I am quite certain that since I did not go through the rigours of a regression analysis and adjusted for errors, that there are easily a lot of improvements that can be made in the metrics tracked and relative multiplier/weight I’ve currently assigned.

I would love nothing more than to push this forward, in any small way I can. One can argue, and correctly I might add, that the additional stats I’d recommend tracking won’t be able to capture the entirety of a defensive possession and what not. This is where my subjectiveness comes in, as those metrics are important for me. You can take this forward and eventually find out that none of these contribute that much toward analysing efficiency, and I’d be thankful still.

#### Why am *I* computing BEQ?

Basketball is almost like a religion in the Philippines, what with makeshift hoops found seemingly around every street corner filled with some of the most knowledgable fans playing the game that they love, showing their devotion through the sheer amount of time spent playing in *mostly* slipper laden feet in a game introduced by the Americans in the 1900’s. Filipinos are Basketball Crazy. No, wait, we’re actually **Craaaaaazzzy** for Basketball. In breaking games down, I’ve noticed that we only use the data found in the boxscores, not digging deeper to reveal hidden tendencies and confirming with data, what the eyes see. Most of these boxscores are in its raw, written form, which means mining this data to apply advanced statistical analysis is a challenge few will take. I am one of those few, if not one of the first to do so. Word of caution though, as I’m NOT a mathematician or a graduate of Statistics. While I loved Stat101 and Stat102 and did pretty well during college (my grade was 3.5 out of a high of 4.0 at the time), I did not get a degree in Statistics. While I also had a Quantitative Analysis class for my Business Major, I cannot AND do not claim to be an expert by virtue of any of my 2 Bachelors degrees.

However, I do have *some* usable experience in using Quantitative Analysis and Statistics in my work as a Consultant and Entrepreneur. I have also had the fortune to complete a Six Sigma Black Belt Certification course, which deals with stats and analyzing those stats to solve real world problems. If you’re a former employee of General Electric (GE), Motorola, or Honeywell (including a lot of BPO’s globally), than Six Sigma is already a part of your life. Succinctly put, Six Sigma is a set of practices originally developed by Motorola and developed to systematically improve processes by eliminating defects. Using statistical analysis is par for the course when using Six Sigma to improve processes. This experience with such statistical analysis plus a passion for basketball has led me to this lengthy, albeit necessary endeavour, if we are to achieve basketball objectives in the Philippines (a basketball craaaazy country, as you may already know).

#### Why not just use the +/-?

Let me quote exactly why I don’t subscribe to its use, direct from the respected Dan Rosenbaum:

It is easy to misinterpret what can be learned from plus/minus data, and I see mistaken analyses using these data more often than not. Teams do not play their players randomly. Match-ups matter. Roles matter. And trying to isolate the contribution of a player or two when ten players are on the floor at a time is a tough statistical feat. Hearing a general manager without extensive experience with statistical analysis is making heavy use of these data sounds to me like a recipe for disaster. Without a strong understanding of statistics, as well as a strong understanding of basketball, it is just too easy for statistics to be more misleading than useful.”

#### Benchmarking with the Gold Standard of Basketball, the NBA

Wait, you’ll ask, *“why are we using metrics developed for the NBA and not contextualized for Philippine Basketball?”* Basketball is basketball is basketball… anywhere in the world. While the rules may vary between leagues and competitions, the metrics used by the NBA, widely acknowledged as where the best players compete, is *the *gold standard.

```
Why should we hold ourselves to any other standard if the NBA is considered the best of the best when it comes to competitive basketball in the world?
```

If we compare ourselves with the best, use the metrics developed for teams and players that compete there, we have a good chance to get better. If we compare ourselves to a lower standard, we don’t gain as much. Go hard, or go home, right? **Meaningful Basketball Metrics, at par with those used in the NBA, is a start in the right direction.**

Let’s get to it then, shall we?

```
BEQ, how do I compute you? You know what the frequently maligned boxscore told the BEQ after several dates? You compute me. Haha. Enough already, right?
```

Let me start off by saying that I tried to incorporate as much of Dean Oliver’s Four Factors in the formula in coming up with a player’s BEQ. Why? Because based on data from EvanZ and his own regression analysis for the Four Factors, it contributes 96% towards Point Differential, which has shown to be a reliable indicator of winning Basketball games.

So, how does it work? Similar to NBA’s PER, created by John Hollinger, BEQ hopes to go beyond the boxscore in evaluating players and how their hard work contributes towards a winning team. I’m going to use data that PER has used previously and see how the player’s rating changes when using the BEQ that I use to evaluate players I’m watching.

### Computing the Basketball Efficiency Quotient (BEQ)

`BEQ`

=((((FGM+FTM+3PMx1.5)+(eFG%x100))x1.55)+(FTRATEx1.10)+(((OREB%x1.5)+DREB%))/2)x1.15+(STL)-(TORatio x1.20)+BLK+STOP%-PF)/(Total Team Possessions/Games Played)x10

```
BEQ Legend: (It means that for every 10 possessions, the player is playing at this level. It can go up or down depending on how the player adjusts to the indicators we're tracking.) *It's also easier to check at which level they're playing at. We could also use a BEQ per 100 possessions by multiplying by 100.
Legendary Level: 37 and up
MVP Level: 32-37
All Star Level: 28-32
Starter Level: 23-28
Rotation Player Level: 19-23
Reserve Player Level: 9-19
Journeyman Level: 0-9
```

#### Components of the BEQ

Still with me so far? Good. Now let’s learn what advanced basketball analytics are all about. Since a lot of smarter people have come before me, allow me to quote their words on the following advanced stats. These are mostly direct quotes from the sites that clearly explain what these metrics are and I encourage you to go to these sites to devour all the information you want re: basketball stats and analytics. Thanks to EvanZ, whose work I used here in trying to explain how to compute “advance” basketball statistics for analytics purposes. I will incorporate EvanZ work in explaining the advanced statistics in describing BEQ. I’ve also used Neal Paine’s work to help define some of the metrics we’re looking at. Please bear with me, if you’re bored already. 🙂

`eFG%`

From BBR: Effective Field Goal Percentage = (FG + 0.5 * 3P) / FGA. This statistic adjusts for the fact that a 3-point field goal is worth one more point than a 2-point field goal. For example, suppose Player A goes 4 for 10 with 2 threes, while Player B goes 5 for 10 with 0 threes. Each player would have 10 points from field goals, and thus would have the same effective field goal percentage (50%).

I gave the eFG% related portion of the formula a multiplier value of 1.55, since it factors in the most when trying to see how efficient a player and team is. Which is short of saying that in BEQ, it contributes about 1.55 times as much weight as any other tracked and measured metric.

You’ll see that made 3 pointers (3PM) are given a multiplier of 1.5 to account for the extra point and benefit of making a 3. Made 3’s are a big part of today’s game and players who make them deservedly get a plus, which is worth 1.5 more than a made 2 pointer (FGM).

`TO%`

`TO%=TOV / (FGA + 0.44 * FTA + TOV)`

TO% is the ratio of a player’s TO to his own team’s possessions expressed as a percentage. Prevent your team from committing turnovers while forcing the other team to commit them is one way to win a game.

For BEQ, however, we’re using Turnover Ratio (TO) instead of TO% to incorporate the no. of AST a player makes as well. TO Ratio contributes towards a team’s high TO% and thus makes TO Ratio a negative stat. A TO robs the Offense of a chance at getting 1–3 possible points (even 4 in the unlikely chance of a 4 point play) AND these lead to points off the TO, which create undesirable 4 (to 6) point swings.

`OREB%`

`OREB% = (ORB * (Tm MP / 5)) / (MP * (Tm ORB + Opp DRB))`

Offensive rebound percentage is an estimate of the percentage of available offensive rebounds a player grabbed while he was on the floor.

`DREB%`

`DREB% = (DRB * (Tm MP / 5)) / (MP * (Tm DRB + Opp ORB))`

Defensive rebound percentage is an estimate of the percentage of available defensive rebounds a player grabbed while he was on the floor.

`TREB%`

`TREB% = (TRB * (Tm MP / 5)) / (MP * (Tm TRB + Opp TRB))`

Total rebound percentage is an estimate of the percentage of available rebounds a player grabbed while he was on the floor.

I added the `OREB%`

and `DREB%`

and divided by 2, with a 1.5 multiplier assigned to OREB since it contributes more towards the Point Differential winning teams create. Offensive Rebounds (OREB) are worth 1.5 more than regular rebounds as the possession is extended. Preventing the Defensive team from rebounding and catching a scrambling Defense usually results in a positive possession for the Offense. Combining both and dividing by 2 now estimates Total Rebounding%, and for the purpose of getting to the BEQ, TREB% is multiplied by 1.15.

`FTRATE`

FTRATE is the rate a team gets itself to the free throw line, regardless of wether they make it or not. What if the team misses ALL its shots? You could also compute FTMRate or Free Throws Made rate, but we include made FT’s when we compute the FG portion of BEQ. Also, based on the data and work of Dean Oliver and confirmed by EvanZ (his regression analysis was very useful), getting to the line and preventing the other team from getting to the line are markers of winning basketball.

`TS%`

To get True Shooting Percentage, we simply use this formula: `TS%=PTS / (2 * TSA)`

. True shooting percentage is a measure of shooting efficiency that takes into account field goals, 3-point field goals, and free throws. To compute True Shooting Attempts, it’s simply `TSA=FGA + 0.44 * FTA`

.

`AST`

Assists do have value, but based on the current data, tracking TO% is more reliable to assess wether a team wins or not. Since Assists are considered when computing for TO Ratio, let’s just say we are considering this, albeit indirectly.

`STL`

Steals (STL) are worth a multiplier of 1.5 since you prevent the opposing team from even taking a shot, you have a chance at creating desirable point swings, high probability of a fastbreak make, and an extra possession where the defense is caught flat-footed.

`BLK`

You could argue that a Block (BLK) shot be at least be as valuable as a steal, given the degree of difficulty and similar advantages as a steal. This is a work in progress so it will be given more thought as we progress.

`STOP%`

From BBR, Stop Percentage = (Stops * Tm MP) / (Tm Poss * MP). The % of opponent individual possessions directly stopped by a player through steals, blocks, forced misses, defensive rebounds, and forced TO. This assumes a player faces 20% of opponent possessions when on the floor; on the one hand this is patently untrue, but on the other hand defense is more of a team-oriented activity anyway. This is the roughest estimate out of all of the advanced stats here. But I’m including it in the BEQ to account for defensive effort.

`PF`

Personal Fouls are deducted as is, with no multiplier effect. There hasn’t been data that shows how the number of fouls contributes to winning a basketball game.

`POSS`

This is how possessions are computed. POSS = FGA + 0.44*FTA + TOV You can then divide this by the total no. of games if the stat you have is for an entire season worth of basketball. We divide by POSS to arrive at BEQ per possession. We arrive at per 10 possessions BEQ by multiplying by 10.

`Usage%`

Usage% (from sportingcharts)

A metric that estimates the percentage of his team’s possessions a player “uses” while he is in the game. As players “use” more possessions, their overall efficiency tends to drop.

Examining Usage Percentage gives us an indication of how efficient a player is given the amount of possessions he uses. What defines a quality player is someone who can have a high Usage Percentage, but still plays at a high rate of efficiency. Teams can look at the Usage Percentage of players on their team, and determine how to balance usage across their lineup to maximize team efficiency.

Note:While we analyze Usage% and its relation to TO%, we don’t use it in the computation of the BEQ. I use it to reveal which high Usage% player also happens to have a low TO%. More on this later.

Even more definitions of “advanced” stats here, if you’re interested.

### What Are Dean Oliver’s Four Factors?

First, let’s learn about the Four Factors, as outlined in Dean Oliver’s work “Basketball On Paper”

Offense / Defense

What are the Four Factors? Basketball-Reference says:

How do basketball teams win games? While searching for an answer to that question, Dean Oliver identified what he called the “Four Factors of Basketball Success”:

Shooting (40%)

Turnovers (25%)

Rebounding (20%)

Free Throws (15%)

```
The number in parentheses is the approximate weight Mr. Oliver assigned each factor. Shooting is the most important factor, followed by turnovers, rebounding, and free throws.
```

These factors can be applied to both a team’s offense and defense, which in a sense gives us eight factors. Let’s take a closer look at how these factors are measured.

**Shooting**

The shooting factor is measured using Effective Field Goal Percentage (eFG%). The formula for both offense and defense is (FG + 0.5 * 3P) / FGA.

**Turnovers**

The turnover factor is measured using Turnover Percentage (TOV%). The formula for both offense and defense is TOV / (FGA + 0.44 * FTA + TOV).

**Rebounding**

The rebounding factor is measured using Offensive and Defensive Rebound Percentage (ORB% and DRB%, respectively). The formula for offense is ORB / (ORB + Opp DRB), while the formula for defense is DRB / (Opp ORB + DRB).

**Free Throws**

The free throw factor is a measure of both how often a team gets to the line and how often they make them. The formula for both offense and defense is FT / FGA.”

Here’s what the creator of the Four Factors, Dean Oliver, has to say about it:

Let’s describe the Four Factors before talking about strategy. First of all, you need four of them. You can’t really describe winning in full without at least four distinct factors. They are four different skills and they are pretty much independent of each other. The first factor, shooting the ball, is the most important. The game of basketball was set up that way more than one hundred years ago, where the objective of that first game in Massachusetts with two peach baskets was nothing more than getting the ball into those baskets. In that essence, the game hasn’t changed. Whether it’s 3-foot shots or 3-point shots, shooting the ball from the field remains the dominant means of scoring points before giving it back to opponents. (See Box for details of how the Four Factors are calculated, not that it’s hard.)

The second factor is taking care of the ball, or avoiding turnovers. This factor can be very important at lower levels of basketball, such as with young kids, where dribbling and passing skills aren’t very well developed. They may not be able to get the ball over half court if these skills aren’t refined, which means that they’re not even able to take shots. But at professional levels, each team often has several players who can bring the ball across half court without significant concerns about losing it. Full court pressure may change this, but it isn’t commonly used much at the highest levels because it isn’t as effective. Nonetheless, turnovers are an underappreciated aspect of pro basketball, with traveling and shot-clock violations rarely getting the outrage (from coaches or commentators) that a bad shot gets.

The third factor is offensive rebounding. If a team can get back its missed shots, it can partially make up for a problem with that first factor. It still eventually has to put the ball in the basket, but giving itself multiple opportunities allows a team a chance when its gunners from the outside are misfiring.

The fourth factor is getting to the foul line. I phrase this intentionally as “getting to the foul line,” not “making foul shots” or “free throw percentage” or “free throws.” This is because the biggest aspect of “free throws” is actually attempting them, not making them. Teams that get to the line more are more effective than teams that make a higher percentage of their free throws. Game-by-game exceptions can definitely exist – there are plenty of games that are lost by a team missing its foul shots – but over the long haul, just getting to the line frequently wins a lot more games than missing a few freebies will lose.

So those are the Four Factors, but don’t forget that it is four each for both the offense and defense. A team’s offense must shoot well, but its defense must also shut down an opponent’s shooting. A team’s offense must follow its own misses with offensive boards, but it also needs to keep its opponents off the glass by getting defensive boards. Our favorite team has its offensive rebounding percentage and the bad guys have their offensive rebounding percentage, which is effectively the same as our team’s defensive rebounding percentage. Knowing one gives you the other but both are important.

In a nutshell, Dean Oliver’s findings show the following:

#### On Offense

To increase your chances at winning a basketball game you need to ensure your team focuses on

shootingthe ball well,take care of the balllike it was gold,reboundthe ball on your own missed shots like no tomorrow, and get fouled enough togo to the line,hitting it being a bonus. This is on the offensive side of the ball.

#### On Defense

Defensively this means the following: lockdown on defense, those closeouts and challenge shots at the rim to bring down eFG%, force turnovers, rebound the ball and allow only 1 shot per trip, and play good defense without fouling.

Wait, what about the weights assigned to each of the Four Factors? Are we sure this data is proven, or even worth considering in reality? Well, thanks to EvanZ and his excellent article on the subject, we can be confident in the weights we’re using to ensure we win games by focusing on these Four Factors. EvanZ said:

There are four factors of an offense or defense that define its efficiency: shooting percentage, turnover rate, offensive rebounding percentage, and getting to the foul line. Striving to control those factors leads to a more successful team. (Dean Oliver, “Basketball on Paper”)

How well do these four factors predict point differential (and thus, winning)? How important are each of the factors relative to the others? So, how well do these four factors predict point differential? Long story short? The four factors (eFG%, TOR, FTR, & ORR) explains about 96% of point differential. It turns out that these four factors (well, 8 factors, if you consider offense and defense, of course) account for a

whopping 96% of point differential. Moreover, of the four factors, I estimated that`eFG%`

accounts for about 54% (40%) of the total,`TO%`

account for 22% (25%),`OREB`

15% (20%), and`FTRate`

10% (15%). The numbers in parentheses refer to the values that Oliver found.

### The Four Factors, in Context and in Action

Now that we know that the four factors account for **96% of the point differential**, and a positive differential correlates with winning, how do we make sense of it in the **context of the Philippines’ performance** last August 2013 at the FIBA-ASIA Tournament?

Let’s take a look at the numbers. The Philippines finished 2nd by improving their overall play compared to years gone by, as obviously evidenced by making it to the FIBA Worlds after 40 years. Yes, 40 years. As a die-hard fan of Basketball and the Philippines, I would love to see the Philippines make it to EVERY major international competition, including the Olympics. How did each player fare during the competition?

I understand that the data set is too small to make sweeping statements, but since this is the only relevant data available at this time, a start in the right direction is better than no start at all.

#### Four Factors and BEQ in Action

Using the data from the boxscores during the FIBA Asia Tournament held in Manila last August 2013, I will try and compute the BEQ of every Gilas player who represented the country and played their hearts out to bring the Philippines to the FIBA Worlds this August 2014. I would have wanted to use the play-by-play to add even more value to the boxscore but time constraints prevent me from doing so at this time.

Each player’s BEQ represents how efficient they were based on 10 possessions, which I used as the baseline to estimate at what level players are playing during the tournament, which means we can *theoretically compare our top players*, who were the most likely members of the Gilas Team, to their NBA counterparts. In a future series, when I have the time, I hope to compute the BEQ of some of the top NBA players and compare them with the BEQ’s of PBA players. This would need to be taken in context though, as they don’t compete in the same league and star players here with a high `Usage%`

will likely have increased numbers across the board as they’re expected to do the bulk of the heavy lifting, considering what their teams are paying them.

First, allow me to present the **Four Factors Metrics** for the entire Gilas Team, both on Offense and Defense. This should show us how we won 2nd place and how we can improve our chances for the FIBA Worlds. So, how did the Philippine-Gilas team fare in this regard? Let’s have a quick look:

#### Four Factors of The Philippine Team 2013.

##### Offense

eFG% | TO% | OREB% | FTRate | ORTG |
---|---|---|---|---|

47.38% | 11.57% | 28.13% | 25.86% | 108.54 |

##### Defense

eFG% | TO% | OREB% | FTRate | DRTG |
---|---|---|---|---|

41.81% | 14.35% | 28.13% | 29.10% | 96.87 |

##### Overall Efficiency (Point Differential)

Point Differential |
---|

11.67 |

Their Offensive and Defensive Efficiency revealed that they had an average **+ Point Differential per 100 possessions of +11.67**. ORTG was 108.54 while their DRTG was 96.87. While we ultimately fell short in the Final game against Iran, these numbers are actually quite efficient. It’s been proven that a positive point differential per 100 possessions is correlates to winning basketball games. Check out EvanZ work here.

In fact, If a team in the NBA were to have such a point differential after an entire season, we could easily estimate the number of wins they’d have using a tested formula the stat geeks have arrived at.

`The "winning" formula is W = 2.54 * p.m. + 40.9.`

So, how many wins would a Team playing at a +11.67 level have over 82 games in the NBA?

Since the Gilas Team has a +11.67 point differential (ORTG-DRTG), we can estimate how many wins they’d have if they were playing at the same efficiency in the NBA. A team playing with a differential like what the Gilas Team has would be estimated to win 70.54 games in an 82 game regular season. 70 wins, yes, you read that right.

An estimated

70–12record for a team playing with a +11.67 differential.

Over time you can expect these numbers to go down as the talent level is at a whole other level in the NBA (assuming the Gilas Team is playing in the NBA, which would be unheard of yet totally cool!), but we can see how well the team played with their **11.67 point differential**. My point is this, **a +11.67 differential will win you basketball games**.

Note:I’m not implying that the Philippines is a better team than those in the NBA, as duplicating such an efficiency against NBA level competition will surely bring those numbers down. But what I am saying is that, based on the data from the August 2013 tournament, we had an Offensive and Defensive Efficiency that would translate into wins in the NBA. If we were to play in the NBA, our numbers would surely find its level as the competition and talent level is at another level. That said, I don’t know any NBA team who will say NO to a point differential of +11.67.

#### Why didn’t we win vs. Iran then, if a +11.67 differential is so good?

So why didn’t we win the gold vs. Iran?

Short of chalking it up to the loss of Douthit in the final game, I will show you how we fared in the four factors and Offensive and Defensive Efficiency in that final game.

#### Four Factors of The Philippines vs. Iran:

##### Offense

eFG% | TO% | OREB% | FTRate | ORTG |
---|---|---|---|---|

39.13% | 11.22% | 24.00% | 33.33% | 79.67 |

##### Defense

eFG% | TO% | OREB% | FTRate | DRTG |
---|---|---|---|---|

51.64% | 19.90% | 39.39% | 42.62% | 93.99 |

##### Overall Efficiency (Point Differential)

Point Differential |
---|

–14.32 |

Marcus would definitely have helped make it a closer game and possibly even help us win it, based on what these advanced metrics revealed. Marcus’ rebounding efficiency and ability to get to the line would have been huge for the team. But this is basketball and injuries happen. How efficient were we vs. Iran? Not so efficient, especially offensively. Defensively, we held Iran to a lower efficiency compared to what we averaged in the entire tournament. Offensively, we were *way off *our average efficiency numbers. We averaged an **ORTG of 108.54** in the entire tournament. In the Final game we had an **ORTG of 79.67**, or almost 29 points less per 100 possessions. Our point differential was a **–14.32 vs. Iran**.

Translating those efficiency ratings to NBA wins, if you play at the same efficiency, we went from playing as efficiently as a 70-win team **(Chicago Bulls 1996 level)** to a 4.52-win team (Almost 3 games worse than the 2012 Bobcats which finished with 7 wins). To say that it’s a tremendous difference would be a huge understatement.

## Note:

Isn’t it ironic that both win records, the famous and infamous, have one person in common: Michael Jordan, although he never suited up for the Bobcats?

Let’s look at what the Four Factors revealed:

#### Offense

Offensively, we had an eFG% of only 39.13%, got to the free throw only 33.33% of the time, rebounded offensively with a 24% rate, and took care of the ball well as evidenced by our 11.22% TO%.

#### Defense

Defensively, we simply couldn’t stop Iran as they posted an eFG% of 51.64%, out-rebounded us on their Offensive glass with a 39.39% OREB% rate. (We did not rebound defensively as well as we could have with Marcus, thus potential DREBs became OREBs for Iran.) They also got themselves to the line at a rate of 42.62%, which is not a good number after averaging a rate of only 29.10% in the entire tournament. We did a good job of forcing Iran to turnovers though, as their TO% was 19.19%.

The four factors revealed that we forced a lot of TO on defense but fouled them too much (Iran’s FTRate was 42.62%) and weren’t effective in preventing them from making their shots more difficult, as shown by their highly efficient eFG% (51.64%). We also weren’t able to rebound the ball defensively, which led to offensive rebounds for Iran (OREB% 39.39%), and weren’t able to rebound efficiently on the offensive end with our 24% OREB%. The numbers clearly weren’t on our side in the final game. I have to commend the team though as we did play with a lot of heart considering we were playing without the player that would have made a huge difference in almost all of the four factors we’ve analyzed.

Would Marcus have made a difference? The metrics show he would have. Stay healthy and let’s go on to the Worlds!

#### Now let’s look at the Player’s BEQ (and other Advanced Stats)

After manually mining the data available, (I transferred the data manually. I wish there were Excel spreadsheets of all boxscores available for download, which would have made my job getting to this point much easier), these are the BEQ’s, along with other advanced statistics, of each respective player. Remember that BEQ tries to incorporate Dean Oliver’s Four Factors and some defensive numbers (estimated until we have actual data tracked, which I suggest moving forward). We will be using these advanced stats to analyze and assess how each player did during the 2013 FIBA-Asia Tournament.

#### Philippines-Gilas Advanced Stats (2013 FIBA-Asia)

Name | BEQ | eFG% | TS% | TO% | TREB% | Usage% |
---|---|---|---|---|---|---|

William | 33.03 | 42.94 | 51.44 | 8.03 | 8.07 | 27.86 |

Chan | 32.15 | 67.24 | 68.61 | 7.72 | 4.40 | 16.46 |

Douthit | 28.69 | 43.21 | 49.73 | 17.31 | 18.58 | 26.45 |

Norwood | 28.21 | 55.26 | 57.68 | 11.18 | 7.52 | 10.35 |

Aguilar | 26.68 | 54.05 | 59.04 | 8.33 | 13.18 | 16.71 |

De Ocampo | 26.28 | 49.32 | 49.70 | 10.79 | 11.23 | 24.05 |

Alapag | 25.10 | 54.35 | 58.56 | 13.91 | 5.72 | 21.70 |

Pingris | 24.63 | 65.52 | 65.23 | 15.40 | 15.59 | 11.69 |

Tenorio | 23.12 | 41.67 | 46.27 | 15.62 | 10.07 | 25.53 |

Fonacier | 23.01 | 46.81 | 49.22 | 7.58 | 5.22 | 16.96 |

David | 19.23 | 34.69 | 38.73 | 1.90 | 7.05 | 28.57 |

Fajardo | 3.98 | 14.29 | 19.04 | 38.82 | 14.77 | 19.53 |

### Where the Proverbial Rubber Meets the Road… (to the Worlds)

#### What do these advanced metrics tell us and how do we interpret the data?

**What my analysis of the metrics revealed:**

**Jayson William (Castro)** has an unusually high Usage% rate WHILE having a very low TO% rate. This means that he’s a very efficient player, which is reflected in having the best BEQ of the entire team. This validates the eye test, when I watched the games on TV, when he looked spry and active on both ends of the floor. He had a better TO% rate than Kevin Martin (8.30) and Jeremy Lamb (8.10) of the NBA while having a higher Usage% than James Harden (Usage% 27.7) of the Rockets, Dwayne Wade (Usage% 27.2) of the Heat, and Tony Parker (Usage% 26.9) of the Spurs.

That’s pretty efficient, considering none of the guys in the NBA with the top Usage% rates (Kevin Durant 32.70 & Lebron James 30.80 Usage% respectively) are in the Top TO%, where low is the the way to go as far as efficiency is concerned.

**Marcus Douthit** is worth his weight in gold and his absence in the final game could have been the difference that could have pushed Gilas to get the gold. Check out Douthit’s `TREB%`

rate and you’ll see that he’s the best rebounder on the team, especially on the defensive end. His TREB% of 18.58% means he grabs available rebounds at that rate when he’s on the floor.

How good is a

`TREB%`

of 18.58? Consider that one of the NBA’s best rebounders, Joakim Noah of the Chicago Bulls, has a`TREB%`

of 18.70%.

His absence in the Final game against Iran made me wonder how he would have changed the game. His numbers in this analysis reveal we missed him a lot and his presence could have made a difference in the game. I know its a small sample size, but that’s really all we have available, until the FIBA Worlds come around, that is. And even with more data from the Worlds, it won’t give us a sample size large enough to gain even more valuable insight so we have to work with what we have. Some data is better than no data at all, in this case.

**Marc Pingris** is a rebounding monster. I know that’s stating the obvious, if you watch him regularly, but looking deeper at the stats here confirm what we see. If you need a rebound to secure the stop, you better have Pingris, known locally by his nickname “Sakuragi”, in the game. He’s second only to Douthit in gobbling up available rebounds and his efficiency stats backup the energy you see on the court. His Rebounding% or `TREB%`

is at 15.59%, 2nd on the team, while he shoots an eFG% of 65.52. This contributed heavily to his `BEQ`

of 24.63. He is also the team’s emotional leader, so much so that where his heart is, the team will follow.

I’m not surprised how his numbers, after taking a deeper look, reflect his efficiency. I can’t stress this enough.

**Jeff Chan’s** shooting efficiency during the 2013 tournament was definitely one for the ages. His stroke is silky smooth, while his eFG% (67.24%) is off the roof. Look at the data comparing the top eFG% in the NBA for the 2014 season and you’ll see just how crazy Jeff’s numbers are. While you can expect this to even out with more data, his ability to keep such an efficient shooting clip during the entire 2013 tournament was a spectacle to see.

How efficient is an eFG% of 67.24%? Consider that his eFG% is better that the NBA’s leaders, including Lebron James, Chris Bosh, and Kevin Durant (61.90%, 56.40%, & 56.30% respectively) . That’s pretty lofty company, eFG% shooting wise.

That doesn’t mean he will do it against NBA level competition, as the competition will definitely be tougher. But it also doesn’t mean he won’t have what it takes to pull it off. 🙂

**Gabe Norwood and Ranidel de Ocampo** deserve to be in the FIBA Worlds Team, if only because they both showed how they keep the team winning by shooting more than 50% eFG% combined and rebounding at a rate of 18.75%. This simply means that when on the floor together, you can expect almost 18.75% of all available rebounds to land in either player’s hands. One last thing about **Gabe Norwood **though. See his low Usage% yet low TO%, high eFG%, and high BEQ? I’d let him have the ball more and tell him to attack the basket and keep shooting when he has a good shot.

It would be prudent to maximize the skills of a player who shoots a high eFG% and turns the ball over at a low rate.

**Jimmy Alapag** and Ranidel de Ocampo are very efficient scorers, based on their high Usage% (21.70 and 24.05) while shooting an eFG% of 54.35 and 49.32 respectively. When a high Usage% player shoots with those eFG% rates, that’s the mark of an efficient scorer.

Having them in the game will lead to efficient shots, either through a direct assist or the pass before the assist.

We also have 2 guards who rebound at a high rate, a combined 18.14%, and if both of them are on the court at the same time, you could have the best rebounding unit without losing scoring efficiency. These guards are Jayson William and **LA Tenorio**. Their combined rate is almost the same as that of Marcus Douthit, by the way. Their `TREB%`

shows their ability to grab the no. of available rebounds when they’re in the game.

That means they grab 18.14% of the total available rebounds when playing together. Those are strong rebounding% rates for guards.

**Japeth Aguilar** needs more minutes, given that he has a high `BEQ`

, at 26.68, while playing only about 15min per game. Ditto for Marc Pingris, who has a BEQ of 24.63 while averaging about 17min per game. If their per 10-possession BEQ is any indicator, giving them more minutes will make them even more productive. Based on this data, starting Aguilar and giving him more than reserve type minutes will bode well for the team. I also strongly recommend, if he isn’t yet, that Pingris be the **first big off the bench**.

Another bonus for giving Japeth more minutes is that data shows he only has a TO% of 8.33, which is indicative of how well he avoided TOs during the tournament, while shooting an eFG% of 54.05%.

If we need a scoring (`eFG%`

of 54.05), efficient rebounding big man (`TREB%`

of 13.18%) that is not prone to turn the ball over, look no further than Japeth.

**Speaking of TO%**, there were three other players who had a good (meaning low) TO%. These were David, Chan, and Fonacier. Surprisingly, **Gary David’s** TO% (1.9%) is the best while having the highest Usage% (28.57) while on the floor! Given that he’s played one of the fewest minutes per game (about 9.5min/game), he could use a few more minutes, if he can bring up his eFG% (34.69%) and improve his rebounding, which is at 1.69%. For a shooter like El Granada, David’s eFG% should be hovering above the 50% rate. His poor shooting and average rebounding rate (not simply the no. of rebounds he gets but how many % of the available rebounds he grabs while on the floor) are what kept him off the floor and on the bench, based on the data we have.

Given the way he takes care of the ball, despite having the highest Usage%, he could be used efficiently as a decoy and willing passer to help generate high percentage shots.

**June Mar Fajardo** didn’t play many minutes, but in the minutes he played he showed a lot of signs of the jitters, based on the data we’ve analyzed. He is in the middle of the pack in terms of Usage%, with 19.53, which simply means when on the floor, he used up 19.53% of the possession. No issues with Usage% alone though, as other bigs have a higher rate than Fajardo’s, with Douthit at 26.45 and De Ocampo at 24.05. My issue is with Fajardo’s TO%, or the percentage of TO he contributes to the total TO for every possession, where he is shown to be the most TO prone with a rate of 38.82%. If your Usage% is anywhere near 20%, it would bode well for you to keep your TO% rate down. Remember **David**, who had the highest Usage% at 28.57? He had a TO% of 1.9%, which means he was not as prone to turn the ball over when he was using a possession.

This means one of 2 things (or both) he passes very efficiently or he jacks up a lot of shots. You can’t get a turnover if you shoot the ball, even if you miss. What tells me he shot the ball a lot, and inefficiently at that, is David’s eFG% of 34.69%, only beating Fajardo, who had an eFG% of 14.29%.

Back to June Mar, he had a Usage% of 19.53, while having the lowest eFG% AND highest TO%? Those rates are a recipe for disaster. Could his rates have risen with more playing time?

Maybe, but the data suggests that on this team, at that time, he was worthy of averaging only 3.85min/game. Again, the data supports the common sense of the eye test.

Fajardo’s`TREB%`

of 14.77 is 3rd on this team, which means he grabs available rebounds at that rate when he’s on the floor, which isn’t that much probably due to his high`TO%`

and abysmal`eFG%`

of 14.29%.

Based on the BEQ and other advanced metrics we’ve computed, we’ve provided data that shows the things the players needed to do to win basketball games and win 2nd place. They combined to shoot a high eFG%, took care of the ball and contribute to a low TO%, and they rebounded the ball offensively to keep the possession alive and have a better than average OREB%. We’ve also broken down, based on relevant data and advanced metrics, how each player performed.

If you notice, the BEQ is another way of looking at the Four Factors, for each player for every 10 possessions, while adding effort on the defensive end by accounting for Steals, Blocks, and Stop% to the equation. Overall, the BEQ confirms and supports what our eyes told us when we watched the games last August 2013 and may prove beneficial in choosing the final team that will represent the Philippines in the FIBA Worlds this 2014.

#### Which players were the most efficient on the floor, based on the metrics we’ve analyzed, and who should we have playing most of the time if we want the best chance to win? (I propose that these guys be shoo-in’s for the FIBA-Worlds Team, if only based on their sterling performance when they represented the Philippines last August 2013)

Based on what the metrics revealed, the most efficient would be: **Jayson William, Jeff Chan, Gabe Norwood, Ranidel De Ocampo, Japeth Aguilar, and Marcus Douthit**. Jayson and Jeff would be 1 and 2, Gabe starts as the 3, Ranidel as the 4, and Marcus as the 5. Japeth would be one of the first bigs off the bench. (Or Ranidel, depending on the priorities of the Coaching Staff)

Four of these six are the highest rated rebounders on the team, Three of them (Jeff, Ranidel, Japeth, and Gabe) are efficient shooters, being in the Top 6 of the team’s eFG%. Jayson’s high usage, low turnover combo (27.86/8.03) is exactly what we need to prevent TO’s and force TO’s on the other end. You know who else is highly efficient when comparing his respective Usage% and TO%? Ranidel, with Usage and TO% rates of 24.05/10.79 respectively. If we believe the data, and I am inclined to at this point, Ranidel and Jayson take care of the ball when they use a possession.

Want to have a high eFG%, limit TO% rate AND rebound at high rate? Put these guys on the court for most of the games, if their performance in 2013 is to be factored in the decision, and we have a chance if they play as efficiently against tougher competition.

Well, I sure hope this long read was worth it, as it’s a dream of mine to be able to help Philippine Basketball in this regard. Again, if any of the Coaches, Managers, or Owners want to discuss how to effectively incorporate this in their respective teams, do feel free to get in touch by following me on Twitter (**@sarcastillo**). It would be interesting to see how these work with more data from your respective teams. I’m pretty confident your data will reveal how you can improve your chances at winning even more basketball games.

Final Note:We still have a long way to go in incorporating defensive effort in a metric to show overall efficiency and thats where aBEQ fits right in. Right now we use Stop% in the BEQ formula to incorporate defense, albeit a little crudely as we’d really need more data to be tracked in order to be more efficient ourselves in accounting for defensive effort.

### Some questions I asked myself (and answered) when working on this:

#### Why should you, coaches and owners involved in the game and the business of Basketball even consider using analytics?

Players, coaches, and owners should embrace analytics beyond the boxscores as it will enable them to measure the right things that lead to winning in basketball. Players whose work doesn’t show in the stats (like the pick setter who consistently gets the shooter uncontested shots or the shooter who spaces the floor by simply being a decoy for the play and even the defensive specialist who takes charges and contests and changes shots regularly) will be happy to see that the aBEQ will take these into account, if not now then eventually.

#### How about penalizing missed FT’s?

We could deduct (or assign a negative value) to missed freethrows (FTA-FTM) at a value of 1.5. When nobody is guarding you, you should (statistically) be 20% more efficient at making the shot. If you have that much time to square up for a shot, missing has to be penalized. However, if you miss a lot of FT, it means you have a high FTRate within the team (one of Dean Oliver’s 4 factors) and you give your team a better chance at winning the game. As such, for BEQ purposes, and since FTRate is already in the BEQ equation. In aBEQ, we will be using Escobar’s WFG% to truly estimate the shooting efficiency of every player.

#### What about missed FG’s and missed 3’s? Shouldn’t we penalize those who keep on taking 3’s and misses at a consistent rate?

We could. In my earlier versions Missed Field Goals (FGA-FGM) and Missed 3 pointers (3PA–3PM) are both “deductibles” from the overall BEQ and reflected by having the same multiplier of .35. Here, missing a 2 and a 3 are assigned the same multiplier since a missed shot, quite simply, is a miss. We don’t factor in the degree of difficulty of the 3 due to its distance or the opportunity cost of missing the 2 vs. missing the 3. I changed my mind (something which will happen a lot in this space) since eFG% covers made and missed FG’s already.

#### Wait, what about defensive effort? How do we ensure defensive specialists get a fair shake when looking at efficiency?

I’m glad you asked. Please welcome the Adjusted BEQ!

### The Adjusted BEQ

How do we begin to account for defense and “making the right play” in measuring BEQ if detailed SportsVU type data is not available and the stats are not on any boxscore? I propose to measure the no. of contests (close-outs/changed shots) each player makes with a multiplier of 0.5 or half of what is currently assigned to BLKs. Pass Deflections, whether it leads to a steal or not, should also be measured to account for defending the passing lanes and making it a challenge for the offense to execute against your team.

Another thing we could measure is Penetrations Stopped, which is simply the number of times a defensive player “stops” penetration (measured by the offensive player with the ball not being able to enter the “painted area”) and enables the defense to remain set and not prone to over-helping and rotations that closing out on the ball usually generates. Some coaches use penetration not to get a shot at the rim, but to scramble a defense and get high percentage shots (open shots from the corner 3 and open shots in general). Stopping penetration is key to successful defense in basketball. We’d assign this the same value as no. of contests or 0.5.

We might also use Escobar’s WFG%, instead of eFG% or TS%, to get the aBEQ. This updated metric can actually be used now for BEQ, but since I wanted to highlight how our players eFG% compare with the best, we’ve stuck to eFG%. Watch out for a future post which will compare BEQ vs. aBEQ with WFG% as the only change.

Since we don’t have some of this data available at the moment, I’ve focused on BEQ for now. Moving forward, if some teams are so inclined to, I would love to see aBEQ in action. We’d need a few more stat guys on the team, but accurately accounting for Efficiency can help maximize a team’s investment in its players. Professional Basketball is, after all, a business as well.

### Additional Defensive Stats needed to compute aBEQ:

- No. of Successful Contests and Challenged Shots (including timely closeouts) which lead to a missed shot = CNTST
- No. of Pass Deflections = DEFLTN
- No. of Penetrations Stopped = PENSTP
- No. of Charges Taken = CHGTKN
- Weighted Field Goal Percentage = WFG%

#### Adjusted BEQ (aBEQ)

`aBEQ`

=((((FGM+FTM+3PMx1.5)+(WFG%x100))x1.55)+(FTRATEx1.10)+(((OREB%x1.5)+DREB%))/2)x1.15+(STL)-(TORatio x1.20)+BLK+(CNTSTx0.5+DEFLTNx0.5+(PENSTPx0.5/TMPoss))x0.25+(CHGTKNx1.5)+STOP%-PF)/(Total Team Possessions/Games Played)x10 possessions

#### Additional Metrics required for Adjusted BEQ (aBEQ)

`CNTST`

No. of Successful Contests and Challenged Shots (including timely closeouts) which lead to a missed shot is considered a contest in aBEQ. Contests lower the expected value of a shooter’s outside shot. We’re looking at contesting shots taken from outside the paint. Experts estimate that a contested shot, if timely and done before a shot can be cleanly taken, lowers the chances of making it by up to 20%. Challenged shots are defined as those taken within the paint. If a player misses a high percentage shot because of a successful challenge, then it’s almost like a blocked shot.

`DEFLTN`

Deflections that impede the progress of an offensive play are what we’re looking at here. If the deflection leads to a steal, then all much the better. Deflections on a an attempted shot are counted as blocks so those don’t count here.

`PENSTP`

We’re looking to reward great on-ball defender’s effort in stopping their man’s penetration (usually a guard or slashing forward). Big men who hedge effectively and stop the penetration off the pick and roll are also rewarded by this metric. Why are we measuring how effective a penetration is stopped? Effective Penetration is the backbone of many efficient offenses in the game today. It causes a defense to scramble and adjust like no other play can. It’s completely disruptive, if allowed by the defensive team. It starts the ball moving from one end to another and usually results in a high percentage shot. We reward those who are able to stop this from happening on a per possession basis.

`CHGTKN`

Charges Taken (CHGTKN) are given importance here as it’s an automatic TO, puts a team in or near the penalty situation, and gets scorers/ball handlers or pick setters (the heart of most offenses nowadays) a foul. It shows the willingness of a defensive player to take one for the team, literally. Flops shouldn’t count against this but until flops are made official by the international game and tracked reliably, we don’t consider it in the equation.

`WFG%`

Weighted Field Goal Percentage, which accounts for every shot (free throw, 2-point shot, and 3-point shot) taken by a player in a given game. The metric weighs these shots (free throw, least; 3-pointer most) and generates a shooting percentage based on a player’s overall shooting performance.

- “Bas Verplanken and Wendy Wood, “Interventions to Break and Create Consumer Habits,” Journal of Public Policy and Marketing 25, no. 1 (2006): 90–103; David T. Neal, Wendy Wood, and Jeffrey M. Quinn, “Habits—A Repeat Performance,” Current Directions in Psychological Science 15, no. 4 (2006): 198–202.” ↩