Link to the PER Rookie Leaders: http://insider.espn.com/nba/hollinger/statistics/_/position/rookies

It’s nice to see Rose get the recognition that he deserves, topping LaMelo with the highest PER of all qualified rookies.

Derrick Rose PER: 20.60

LaMelo Ball PER: 17.61

PER
He's 3rd in BPM, what a bum.

Edit: Thanks /u/P0intcenter

With the confirmed suspension for the last game of the season, Giannis has officially clinched the highest single season mark for Player Efficiency Rating at 31.91, once considered the best all-in-one advanced stat. List of every player to achieve over 30.0 PER in a season below:

https://www.basketball-reference.com/leaders/per_season.html

Top 15

1- Joel Embiid (31.18)

2- Nikola Jokic (30.91)

3- Giannis Antetokounmpo (28.26)

4- Kawhi Leonard (27.74)

5- Luka Doncic (26.94)

6- Damian Lillard (26.42)

7- Zion Williamson (25.85)

8- Stephen Curry (25.73)

9- Bradley Beal (25.01)

10- Anthony Davis (24.79)

11- Nikola Vucevic (24.23%)

12- LeBron James (24.20)

13- Chris Boucher (23.58)

14- Robert Williams III (23.58)

15- Jaylen Brown (23.40)

Source: https://www.espn.com/nba/stats/player/_/table/general/sort/PER/dir/desc

Source

> Giannis Antetokounmpo is averaging 30.0 PPG, 13.5 RPG and 5.8 APG this season.

> His Player Efficiency Rating (PER) is 32.3. That’s on track to be the highest PER in NBA history.

His team also has the best record in the league. Back to back MVP?

Immanuel Quickley leads ALL rookies in PER (Player Efficiency Rating):

1. Immanuel Quickley (23.20)
2. Tyrese Haliburton (18.59)
3. LaMelo Ball (16.51)
4. Facundo Campazzo (15.09)
5. Precious Achiuwa (13.68)

Source

#Majors: link

##Minors: link

introduction: https://redd.it/dty8vu

Using Linear Regression with NBA 2017-2018 Player Statistics

I am currently pursuing my Master's in Computational Science and for my final project in one of my courses, I wanted to take my love of basketball and statistics and use the material that I had learned throughout the semester to showcase the relationship between Minutes Player (MP) and the Player efficiency rating (PER). This was a very last minute project, as my first attempt at a project ended up being a failure and I decided to do my project based on something that I enjoy (though I still ended up getting an A on the project, so I must had done something right)!

Some notes to be made:

• I used a dataset I found on Kaggle (https://www.kaggle.com/mcamli/nba17-18/data) that took the stats from the 2017-2018 NBA season via Basketball Reference and complied it into a dataset
• The players who played the most minutes that year were Lebron James, Khris Middleton, Andrew Wiggins, Bradley Beal, and Jrue Holiday (with Lebron being the only one to play more than three thousand minutes). Lebron James was the only player out of the top five players when it came to Minutes Played (PD) to be above the fitted line
• There are a few outliers when it comes to the method that I did; especially when it comes to players who barely played any minutes. Naz Mitrou-Long has the highest PER out of everyone as he only played a minute for the Utah Jazz the whole season, attempted a three point shot that made it in, and got waived afterwards and didn't see another minute on a NBA court that season (I couldn't find footage of this shot anywhere, but if anyone knows if there is a clip of it that exists, let me know!).
• There are ways I want to improve this test in the near future (such as setting a limit for the amount of minutes played, as the players who only played a few minutes will either have an amazing PER or a bad PER, since their action is limited and to base it off of averages rather than total amount)
• I would also like to find a way to showcase what player is being shown whenever you hover over a dot on the graph (and find a cleaner way to display the data)

EDIT: I am taking the suggestions that people have made seriously here. As I've stated, this was a last ditch effort project for a class (though it was my first idea) and I knew while working on the project that there were a bunch of ways that I could easily improve the test if I put more time into and more research on

Hello there, let's talk about stats.

GASP and NISH are bad. Real bad. Made years ago, using really basic stats and arbitrary weights, they offer little information besides that which can be inferred from just checking caps and returns leaderboards (or in NISH's case, to Syniikal's delight, K/D). Yet, for some reason, they're still unironically used by players to compare their peers and decide awards. In the past few months I've had a few lengthy discussions describing in detail the problems with both systems, which if you want to read you can find in my reddit comments (just search GASP). Today I finally finished something I've been working on since, which while not fully solving the problem of TagPro stats, I hope will at least explain and sell people on my approach to creating a better model. Introducing a rudimentary version of TagPro's equivalent of NBA's Player Efficiency Rating - oPER.

If seeing math brings back deeply repressed memories and you just want the pretty final numbers, skip to section V for a ELI0IQ.

#I. The Background

If you're not familiar with NBA's PER, the idea is this: Take all of player's actions, covert them into points secured for their team and divide everything by minutes played*. Essentially what you want is a single number that tells you how many points a player generates through their actions for every minute they're on the court.

The actual process is very detailed and would take ages to explain (some of it is even pretty much impossible) but the main gist is you take the otherwise basic stats and you assign precise values to them based on how likely they are to produce extra points for the player's team or their opponents. Besides the obvious, like "Points Scored", PER looks to evaluate stuff like Rebounds, Missed Shots, Blocks, Steals, Fouls, Turnovers, etc. in terms of how often they lead to a gain or loss of possession, which is then translated into expected points gained or lost due to having or losing the ball.

I attempted to translate the following method into TagPro, using the most complete database of "advanced" stats we have, aka the TagroLeague. Unfortunately this limits me to seasons 10 onward (and only matches recorded on the website). Because of the limitations of basic stats (like the ones used by GASP or NISH) including matches that don't have the extra TPL data would ruin the results. What followed was me spending 90% of the time

Hey everyone,

For context, I started a series in r/nba where I wanted to go through different advanced metrics and break down how they're calculated and what they do well (or don't do well). Someone over there suggested I post here as well, so here it is.

For the first post in this series, I wanted to look at a stat that is more commonly used but still may seem a little confusing to someone that's new to basketball. And for those of us familiar with it, we probably still don't know exactly what ingredients go into pumping out the numbers we end up seeing. So with that, let's take a look at Player Efficiency Rating, or PER.

The Basics

PER is a stat created by John Hollinger (formerly of ESPN, currently with the Memphis Grizzlies), that attempts to encapsulate the entirety of a player's performance per minute into one single number, relative to the rest of the league (league average PER is always 15), while adjusting for pace. It is a series of terms (some positive, some negative) that are added together, resulting in one number that represents that player's contribution to his team.

The Formula

So with that, let's dive into the nitty gritty. I'll list out the formula (as found on basketball-reference.com), and below that I will define each of the variables and take a closer look at what it's doing.

uPER = ( 1 / MP ) * [ 3P + ( 2 / 3 ) * AST + ( 2 - factor * ( team_AST / team_FG ) ) * FG + ( FT * 0.5 * ( 1 + ( 1 - ( team_AST / team_FG ) ) + ( 2 / 3 ) * ( team_AST / team_FG ) ) ) - VOP * TOV - VOP * DRB% * ( FGA - FG ) - VOP * 0.44 * ( 0.44 + ( 0.56 * DRB% ) ) * ( FTA - FT ) + VOP * ( 1 - DRB% ) * ( TRB - ORB ) + VOP * DRB% * ORB + VOP * STL + VOP * DRB% * BLK - PF * ( ( lg_FT / lg_PF ) - 0.44 * ( lg_FTA / lg_PF ) * VOP ) ]

WHEW! That, my friends, is a long equation. BUT WAIT! THERE'S MORE! First of all, some of you probably noticed that this formula hasn't accounted for pace anywhere. What's more, you might have noticed that absolutely nowhere in the formula is the number 15. "But Rob, didn't you tell me this was a pace-adjusted formula and didn't you say that it's normalized to a league average of 15?" Congrats! You'd be correct. What is listed above is actually "unadjusted PER," meaning that we haven't accounted for pace yet, or normalized it. So to get there we need to take two more steps: first we adjust for pace, which we can call aPER:

aPER = uPER * lg_Pace / tm_Pace

Then, we normaliz

Source: Baby Bears, Next Draymond, and the NBA's Most Impressive Rookies So Far via the Ringer.

For those of you keeping score at home, that's:

• .689 TS%

• .671 eFG%

• 23% DRB%

• 1.2 WS

• 22.9 PER

• 0.4 VORP

• 15.2 PIE

• In 22.3 MPG

Also, shoutout to Miami's Chris Silva, who is edging Clarke out in a few categories with a little under half the total minutes played (.699 TS%, .676 eFG%, and 23.2% DRB%).

This is crazy. Wilt holds the second as well but then Jordans 86-87 year is third followed by LeBrons 08-09 year.

He is leading the league in PER in the month of January with 36. His team is 5-2 in that stretch. His TS% is .67 & an ORtg of 127.

This big goofy 21 year old white kid is currently leading the Nuggets to the playoffs. Who would have thought.

Tbh, Jordan and James are pretty good, but shocked that they are even in the same conversation and MarGOATovic. Just feel blessed that I was alive to witness the beginning of Boban's prime.

Source

#I. PER v1.0: https://redd.it/dty8vu

Five months ago I made a rating using TPL's "advanced" stats, which measured players' expected captures based on their individual actions. It basically tried to translate each of the raw stats into a number of caps gained or lost as a result. Due to the limitations of using TPL as the database the final contribution had to be approximated, based mainly on the average distribution of the league's stats. For example a player's successful handoffs were purely based on the average success rate of the entire league. This, along with other aspects of v1.0 meant that I considered it to be more of a proof of concept than a complete version of the rating.

Since then we've had significant progress with detailed .eu stats, which allow us to find exact numbers for many of the v1.0 estimated values. As such, I'm happy to present to you v2.0 of the Player Efficiency Rating.

#II. Changes

Firstly, we're no longer looking at approximated values but rather the exact number of caps generated by players. We can split those into caps and assists. The former are self-explanatory, the latter are teammates' caps made possible through player's actions such as handoffs, regrab, prevent or returns.

Secondly, we're dealing with totals and not per-minute stats, to make single season and weekly comparisons more meaningful. It allows us to judge who contributed most caps in a week or in regular season, rather than just relative to their time played. Season's Total Stats are the exception. Due to extra playoffs games they are presented as per-minute ratings.

Thirdly, I'll be using a different format for the final numbers. I've experimented with a lot of different approaches and found Balka's s6 to be the best. It assigns a value of 50.0 to the average score and a difference of 10.0 for one standard deviation from that mean.

Lastly, since we're using .eu stats I can mirror all the calculations to get a defensive rating for caps conceded rather than scored. Hence we'll be dealing with 3 main ratings here: offensive PER, defensive PER, and total PER being a combination of both.

#III. Calculations

Let's start off with the easiest one: oPER. We can split offensive contributions between caps and assists, which sum up to points - scored by your team directly thanks to you. If we were to leave it at that it wouldn't differ from what we already have displayed on anom's stats sheet so let's go a little bit further here.

All caps are eq

stat via ESPN power rankings

Crazy to see such dominance out of the young guys in the league. I have compiled a season-by-season look at the top 5 guys in efficiency from each year, as well as their age and the averages amongst each group. I have only included players who also qualify for the minutes/game leaderboard; advanced statistics only date back to the 1951-52 season, otherwise I would have included them in the list.

While scrolling through r/nba today in church, I was taken aback - not by the rampant homoeroticism for Miles Plumlee's three-pointer (not even top 5 most attractive on the Nuggets tbh wtf guys) - but by r/nba's constant inability to comprehend advanced statistics.

Some problems (like homosexuality, according to my pastor) can't be solved. But a lack of understanding of advanced statistics can be.

Advanced statistics, as the name advanced would suggest, are complicated. At the end I'll make a tl;dr as brief as I can for the Mike Tyson wannabes out there who don't like reading.

Player Efficiency Rating (PER)

Player Efficiency Rating, or PER, was developed by John Hollinger, a very intelligent man who works in the Memphis front office (can you be intelligent and work for the Grizzlies at the same time?). It's designed to quantify a player's total contribution with one number.

Positives:

PER takes into account every box-score statistic there is, which allows for a fairly complete look at a player's statistics. Every season, it is adjusted so that the league average PER is 15. This makes comparing across eras easier.

It also allows a player's total worth to be summed up into one number evaluating their impact. Although no method of doing this can be complete, PER comes the closest. A 35 PER indicates an all time great season, anything above 25 an All-Star, 15 an average player, and below 10 for players who shouldn't be in the league.

Negatives:

Like a Dwight Howard post up, PER can work sometimes - but if it's your only tool, you're screwed. PER is per minute, not per game, and does not account for opponent strength. This inflates the PER of bench players and players who don't play very many minutes.

For example, Boban Marjanovic's career PER is 27.8, which would put him in MVP contention. However, when taken in context this just shows that Boban is dominant in the limited minutes he player. Good, sure; MVP candidate, no.

Also, PER only takes into account box score defensive statistics like steals and blocks, giving guys who gamble for steals like Russell Westbrook or blocks like Javale McGee an inflated defensive PER.

TL;DR

PER is PER-haps (haha) the best single number to evaluate a player by, but it's not perfect. It overrates bench players who play low minutes and doesn't throughly evaluate defensive performance.

New Mexico ran the ball 46 times for 271 yards and 2 touchdowns, and also forced five turnovers. Also, according to College Football Reference, it was a particularly bad day for UTEP running back Wesley Phillips, who carried the ball ten times for -71 yards, a truly impressive feat.

That's really it, just a pretty funny game I found searching through the old CFB games looking for something to write a post about.

Lebron's statline, a game in which a player gets at least 27 points, 7 rebounds and 7 assists per game is famous because of his career average of 27-7-7, despite the fact that he himself has never had an exact lebron in a game, has once again shown itself in his Player Efficiency rating. As of the all star break, he has a PER of 27.77, on a statline of 26.5/8.9/8.1

http://www.espn.com/nba/player/stats/_/id/1966/lebron-jame (no the link isn't broken, espn just apparently thinks his name is lebron jame)

Seems LeBrons obsession with 27’s and 7’s carries over to his advanced stats too. He should probably just wear 27 out in LA

Source

Hey everyone,

For the first post in this series, I wanted to look at a stat that is more commonly used but still may seem a little confusing to someone that's new to basketball. And for those of us familiar with it, we probably still don't know exactly what ingredients go into pumping out the numbers we end up seeing. So with that, let's take a look at Player Efficiency Rating, or PER.

The Basics

PER is a stat created by John Hollinger (formerly of ESPN, currently with the Memphis Grizzlies), that attempts to encapsulate the entirety of a player's performance per minute into one single number, relative to the rest of the league (league average PER is always 15), while adjusting for pace. It is a series of terms (some positive, some negative) that are added together, resulting in one number that represents that player's contribution to his team.

The Formula

So with that, let's dive into the nitty gritty. I'll list out the formula (as found on basketball-reference.com), and below that I will define each of the variables and take a closer look at what it's doing.

uPER = ( 1 / MP ) * [ 3P + ( 2 / 3 ) * AST + ( 2 - factor * ( team_AST / team_FG ) ) * FG + ( FT * 0.5 * ( 1 + ( 1 - ( team_AST / team_FG ) ) + ( 2 / 3 ) * ( team_AST / team_FG ) ) ) - VOP * TOV - VOP * DRB% * ( FGA - FG ) - VOP * 0.44 * ( 0.44 + ( 0.56 * DRB% ) ) * ( FTA - FT ) + VOP * ( 1 - DRB% ) * ( TRB - ORB ) + VOP * DRB% * ORB + VOP * STL + VOP * DRB% * BLK - PF * ( ( lg_FT / lg_PF ) - 0.44 * ( lg_FTA / lg_PF ) * VOP ) ]

WHEW! That, my friends, is a long equation. BUT WAIT! THERE'S MORE! First of all, some of you probably noticed that this formula hasn't accounted for pace anywhere. What's more, you might have noticed that absolutely nowhere in the formula is the number 15. "But Rob, didn't you tell me this was a pace-adjusted formula and didn't you say that it's normalized to a league average of 15?" Congrats! You'd be correct. What is listed above is actually "unadjusted PER," meaning that we haven't accounted for pace yet, or normalized it. So to get there we need to take two more steps: first we adjust for pace, which we can call aPER:

aPER = uPER * lg_Pace / tm_Pace

Then, we normalize it at 15 being the league average to get our final PER number, which is what you see when you look up the stat online:

PER = aPER * (15 / lg_aPER )

Congratulations on making it this far! Now, you might have realized that in the a

[Source] (http://insider.espn.com/nba/hollinger/statistics)

Boban is dominating the league so far and the MVP talk is clearly warranted. When was the last time we saw a player so much better than the rest of the league?

https://www.basketball-reference.com/leaders/per_career.html