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2016-17 NBA Player Projections Added to Basketball Reference

18th October 2016

We have added 2016-17 NBA player projections, using our Simple Projection System, adapted from Tom Tango's Marcel the Monkey Forecasting System.

Please bear in mind that these projections are being published because (1) they were easy and (2) they were fun. That said, we do believe these results will hold up quite well when compared at the end of the season to other (and much more sophisticated) projection systems.

Since we're not controlling substitution patterns, all projections are for per-36 minutes statistics. Please use these responsibly and enjoy!

Posted in Announcement, Basketball-Reference.com, Data, Features, Statgeekery | No Comments »

What the Heck is Corsi? A Primer on Advanced Hockey Statistics

13th October 2016

Good news for fans of zambonis, fighting, and the greatest video game of the 1990s: the NHL has finally returned! After a wild season last year, there are all kinds of juicy storylines to follow this year. Can the Pittsburgh Penguins become the first back-to-back Stanley Cup winners since the Detroit Red Wings of the 1990s? How will the San Jose Sharks bounce back from coming so close and falling short. Will Alex Ovechkin reach 1,000 goals? Can Connor McDavid build upon a promising rookie year and live up to the hype? What round of the Eastern Conference Playoffs will the Washington Capitals be eliminated in this year (I kid, I kid)?

This blog post will seek to answer none of those. Instead, this week, I wanted to dig into one of the major trends that's been sweeping across the NHL the last few years, among fans and front offices alike. I'm talking, of course, about the rise of advanced statistics.

If you're a sports fan, you're probably at least vaguely familiar with Moneyball and the advanced stat wars in baseball. And you may have read articles about how thinkers in other sports, like basketball, have used similar principles to deepen their understanding of the game. This movement has reached hockey in recent years, as researchers have uncovered several new ways of understanding the game beyond the traditional stats like goals, assists, and plus/minus. These new analytics can help us understand why a team is over or under-performing, and whether that performance is sustainable. They can also help us appreciate unsung players who do more for their team than we may realize, because they don't put up flashy traditional numbers.

So, with that in mind, here's some of the basics to get you started in the world of advanced hockey stats. Read the rest of this entry

Posted in Advanced Stats, Announcement, Hockey-Reference.com, Stat Questions, Statgeekery | 2 Comments »

Explaining our Handling of “Holds”

24th February 2016

UPDATE (Feb. 25, 2016): MLB has informed us that they will be updating Brach's 2015 holds total to 15 (matching us). MLB's Cory Schwartz commented: "We do credit Holds whenever the pitcher enters in a Save situation and leaves with the lead intact, so this was an oversight on our part."

It recently came to our attention that for the 2015 season, we credited Brad Brach with 15 holds. MLB, meanwhile, credited Brach with just 14 holds (NOTE: After reading this post, MLB has agreed that 15 is the correct number of holds for Brach in 2015). It was discovered that the difference was in the handling of the Orioles 5-4 win over the Mariners on May 21. Before we jump into the details, let's examine MLB's definition of a hold (bolding is ours, for emphasis):

"The hold is not an official statistic, but it was created as a way to credit middle relief pitchers for a job well done. Starting pitchers get wins, and closers -- the relief pitchers who come in at the end of the game -- get saves, but the guys who pitch in between the two rarely get either statistic. So what's the most important thing one of these middle relievers can do? "Hold" a lead. If a reliever comes into a game to protect a lead, gets at least one out and leaves without giving up that lead, he gets a hold. But you can't get a save and a hold at the same time."

UPDATE (Feb. 26, 2016): Please see MLB's updated Holds definition here

As you can see, this isn't really much of a definition at all. There's little in the way of criteria here, and it's also pointed out that the statistic isn't even official, anyways. In fact, there's enough confusion that MLB.com credits Cory Rasmus with 2 holds in 2015, but Elias (MLB's official statistician) credits him with 1 hold in 2015. We credit him with 2, for what it's worth. This "definition" provides enough room for interpretation that variance in recorded totals is not uncommon.

Being that the statistic is unofficial, explaining all of this might be a pointless exercise, but in an effort to be transparent, we at least want to point out what standard we are using to assign holds.

Our standard is to give a pitcher a hold any time they protect a lead in a save situation (meaning they could have been eligible for a save if they finished the game). Brach presents an interesting study in that May 21 game. Starter Chris Tillman pitched 3 innings and left with a 4-1 lead. Obviously, he was not eligible for the win due to Rule 10.17(b), as he did not complete 5 innings. Tillman was relieved by Brian Matusz, who allowed 2 runs in the 4th, but completed the inning of work and left the game leading 4-3, when Brach took the mound for the 5th inning. Brach completed 2 scoreless innings, but the Mariners tied it up in the 7th after Brach left the game. The Orioles eventually won the game.

With the benefit of hindsight, you could say that Brach would have been in line for the win (not the save) if he had finished the game, since he ended up being more "effective" than Matusz, which would make it nearly a lock that the official scorer would have given him the win. But, hypothetically, Brach could have given up 20 runs in relief, but maintained the lead, and earned the save (with Matusz getting the win). As unlikely as that scenario is, the point here is that we're not using hindsight in assigning holds. In our opinion, the opportunity for a hold is defined when you enter the game and is only removed retroactively if you are given the win.

To be as clear as possible: our policy is to credit a hold when a pitcher enters the game in a save situation and leaves with the lead (and is not later given the win by the official scorer).

As we bolded in MLB's definition of a hold, "If a reliever comes into a game to protect a lead, gets at least one out and leaves without giving up that lead, he gets a hold." It would sure seem to us that Brach's May 21st appearance fits that criteria.

Posted in Announcement, Baseball-Reference.com, FAQ, Ridiculousness, Stat Questions, Statgeekery | 10 Comments »

Try Our New Passer Rating Calculator

18th February 2016

We have added an NFL Passer Rating Calculator to our frivolities page which will allow users to calculate NFL passer ratings on the fly. All you need to know to calculate a passer rating are attempts, completions, yards, touchdowns and interceptions. A "perfect" rating comes out to 158.3.

Posted in Announcement, Features, Pro-Football-Reference.com, Statgeekery | Comments Off on Try Our New Passer Rating Calculator

2015 Approximate Value Finalized

3rd February 2016

With the list of 2015 Pro Bowlers added, we were able to complete Approximate Value numbers for 2015. As you were perhaps aware, we had previously released a Provisional 2015 AV, but this work has now been completed with the addition of the Pro Bowl data. As anticipated, the values have not been altered in any significant way with the added values. In fact, the top 20 remained the same. J.J. Watt remains the 2015 leader with an AV of 21, and his 88 AV through five seasons remains the most since 1960 (the first season for which we calculate AV).

Posted in Advanced Stats, Announcement, Data, Pro-Football-Reference.com, Statgeekery | 1 Comment »

NHL Player Similarity Scores Updated Through 2014-15

3rd February 2016

We just wanted to quickly note that Similarity Scores have been updated on player pages through 2014-15. Similarity Scores attempts to find players whose careers were similar in terms of quality and shape (but not style of play). If you go to Alex Ovechkin's page and scroll down to the Similarity Scores, you'll see that through ten seasons, his career been most similar to Jaromir Jagr's. When comparing entire careers, he scores as most similar to Sidney Crosby.

 

 

Posted in Announcement, Data, Hockey-Reference.com, Statgeekery | Comments Off on NHL Player Similarity Scores Updated Through 2014-15

Advanced Hockey Stats on Hockey Reference

18th January 2016

If you're among the NHL fans lamenting the upcoming shuttering of War on Ice, we just wanted to remind you of the variety of advanced hockey stats we offer on Hockey Reference. While we're perhaps best known as a repository for historical statistics, we have beefed up our analytics in the last few years. In fact, if you click here, you can conveniently see a menu of our analytics offerings. These include:

Posted in Advanced Stats, Announcement, Data, Hockey-Reference.com, Play Index, Statgeekery | Comments Off on Advanced Hockey Stats on Hockey Reference

LeBron Passes Jordan to Become All-Time VORP King

15th January 2016

LeBron James has moved his career VORP total to 104.46 and now narrowly leads Michael Jordan's 104.44 for most in NBA history. It should be noted that VORP can only be calculated since 1973-74, so Wilt Chamberlain's career is not included (nor are the first four seasons of Kareem Abdul-Jabbar's career). VORP was created by Daniel Myers, in conjunction with Box Plus/Minus. Descriptions of the statistics and how they are calculated can be found here.

A comparison of some of their career regular-season advanced statistics can be seen below:

Player G MP TS% TRB% AST% STL% BLK% TOV% USG% OBPM DBPM BPM VORP
LeBron James 947 37062 .581 10.8 34.4 2.3 1.6 12.5 31.7 7.3 1.9 9.2 104.5
Michael Jordan* 1072 41011 .569 9.4 24.9 3.1 1.4 9.3 33.3 7.0 1.1 8.1 104.4
Provided by Basketball-Reference.com: View Original Table
Generated 1/15/2016.

As you can see, LeBron has been a slightly more efficient shooter, a slightly better rebounder and a significantly more prolific passer. Jordan, on the other hand, took better care of the ball, had greater usage, and had an edge in steals. Still, while LeBron has a slight 7.3 to 7.0 edge in Offensive Box Plus/Minus, it's his decisive 1.9 to 1.1 edge in Defensive BPM which gives him the edge in VORP despite playing about 4,000 fewer minutes than MJ. This is largely the result of LeBron playing for superior defensive teams throughout his career. When Jordan was winning titles in Chicago, they were elite defensively, but that was not always the case earlier (or later) in his career. Another factor, according to Myers, is that "Jordan's offensive stats look to the regression more like a pure offensive player than LeBron, possibly because they are more guard like. And guards usually have a bit less value on the defensive end."

While VORP is a cumulative stat, BPM is a rate stat which serves as the foundation for VORP. LeBron's 9.2 BPM seems to dwarf Jordan's 8.1 BPM. However, Jordan's BPM is weighed down by his geriatric years in Washington. A more fair comparison might be Jordan's 13 seasons in Chicago compared to LeBron's career (he's currently in his 13th season). As you can see, the numbers are more comparable, with LeBron owning a 9.2 to 9.0 edge in BPM and a 104.5 to 99.8 edge in VORP thanks to his 1,175-minute advantage in playing time (remember, Jordan missed the majority of the 1985-86 and 1994-95 seasons):
Player G MP TS% TRB% AST% STL% BLK% TOV% USG% OBPM DBPM BPM VORP
LeBron James 947 37062 .581 10.8 34.4 2.3 1.6 12.5 31.7 7.3 1.9 9.2 104.5
Michael Jordan* 930 35887 .580 9.4 24.9 3.3 1.5 9.3 33.5 7.7 1.3 9.0 99.8
Provided by Basketball-Reference.com: View Original Table
Generated 1/15/2016.
VORP and BPM are not the only advanced metrics we have on the site, however. In the eyes of Win Shares, LeBron still has a lot of work to do in order to catch His Airness. And Jordan himself is only fourth all-time. MJ is, however, the all-time leader in Win Shares per 48 minutes (while LeBron is 6th). Here are their career Win Share statistics compared, followed by a comparison of LeBron to Jordan's 13 seasons in Chicago:
Player G MP PER OWS DWS WS WS/48
LeBron James 947 37062 27.6 128.6 56.6 185.2 .240
Michael Jordan* 1072 41011 27.9 149.9 64.1 214.0 .250
Provided by Basketball-Reference.com: View Original Table
Generated 1/15/2016.
Player G MP PER OWS DWS WS WS/48
LeBron James 947 37062 27.6 128.6 56.6 185.2 .240
Michael Jordan* 930 35887 29.1 145.8 58.7 204.5 .274
Provided by Basketball-Reference.com: View Original Table
Generated 1/15/2016.
We're agnostics in the greatest of all time arguments, but we wanted to share this information with our users  as we noticed that VORP now has a new King.

Posted in Advanced Stats, Announcement, Basketball-Reference.com, History, Leaders, Statgeekery | 12 Comments »

Inside the Warriors’ Small-Ball Death Star

17th December 2015

The first quarter of the 2015-16 NBA season has been dominated by one team: the Golden State Warriors. At 25-1, they've posted the best start in NBA history, with the longest winning streak to start a season and the most wins by a team through its first 26 games. What's transformed the Warriors from an already great team to, potentially, one of the best ever?

You can point to any number of reasons, but a big one is the rise of their unstoppable small-ball lineup. Though an injury to Harrison Barnes caused them to temporarily shelve the lineup, the Warriors' small-ball group has been one of the major stories of the season and figuring out how to stop it will be the main task of any team hoping to unseat the champs. So let's go inside the NBA's equivalent of the Death Star and witness the firepower of this fully armed and operational lineup.

warriors

 

Read the rest of this entry

Posted in Basketball-Reference.com, Play Index, Statgeekery | Comments Off on Inside the Warriors’ Small-Ball Death Star

2015-16 Simple Projection System Projections Are Live

14th October 2015

It's hard to believe, but we're under 2 weeks away from the start of the NBA season. That means it's also time to unveil the SPS projections for 2015-16!

What is Simple Projection System? Simply put, SPS is a very basic method for projecting basketball stats. SPS weighs the last 3 seasons by a factor of 6/3/1 and factors age and a little regression to the mean to project what a player will do in the upcoming season (you can read a more detailed explanation of how it's calculated on this page). If this rings a bell, it may be because you're a baseball fan who is familiar with Tom Tango's Marcel projection system. This quote from Tango pretty succinctly describes his projection system's raison d'etre (and why it's called "Marcel"):

"[I]t is the most basic forecasting system you can have, that uses as little intelligence as possible. So, that's the allusion to the monkey."

SPS applies the same general principles to basketball, though the name has no connection to a 1990s sitcom. As the name would suggest, SPS' calculation is relatively simple, but it also does a pretty decent job and holds its own, even compared to more sophisticated projection systems. Of course, SPS can't take into account contextual changes such as a player moving to a new team, changing coaches, or getting new teammates, but it's a fun starting point as we gear up for another NBA season. It's also important to note that SPS' numbers are per 36 minutes.

So, without further ado, here's who SPS projects to lead the NBA in points per 36 minutes:

Per 36 Minutes
Rk Player PTS ▾
1 Kevin Durant 27.8
2 Russell Westbrook 27.4
3 James Harden 25.6
4 LeBron James 24.9
5 Carmelo Anthony 24.7
6 DeMarcus Cousins 24.6
7 Stephen Curry 24.6
8 Anthony Davis 23.0
9 LaMarcus Aldridge 22.8
10 Blake Griffin 22.7
Provided by Basketball-Reference.com: View Original Table
Generated 10/14/2015.

 

SPS is looking at strong bounceback seasons for KD and Carmelo, while expecting James Harden to outscore Stephen Curry again. Here's the rebound leaders, where SPS projects big things for Hassan Whiteside following his 2014-15 breakout:

Per 36 Minutes
Rk Player Type TRB ▾
1 Andre Drummond Projected 15.4
2 DeAndre Jordan Projected 14.5
3 Hassan Whiteside Projected 14.2
4 Reggie Evans Projected 13.4
5 Omer Asik Projected 13.3
6 DeMarcus Cousins Projected 12.9
7 Rudy Gobert Projected 12.7
8 Tyson Chandler Projected 12.5
9 Thomas Robinson Projected 12.5
10 Dwight Howard Projected 12.4
Provided by Basketball-Reference.com: View Original Table
Generated 10/14/2015.

 

Finally, here are the projected leaders in assists per 36 minutes:

Per 36 Minutes
Rk Player AST ▾
1 Chris Paul 10.3
2 Rajon Rondo 9.5
3 John Wall 9.4
4 Ricky Rubio 9.3
5 Ty Lawson 9.0
6 Kendall Marshall 9.0
7 Russell Westbrook 8.4
8 Stephen Curry 8.1
9 Jeff Teague 7.8
10 Brandon Jennings 7.7
Provided by Basketball-Reference.com: View Original Table
Generated 10/14/2015.

 

If you're anything like me, you were probably surprised to see Kendall Marshall there, but his per 36 assist numbers have been crazy good for the last 3 seasons.

In case you're curious about how SPS has done in the past, we have tables for SPS going back to 1980-81 showing what the projections would be and how they'd compare to the actual results. For example, here's how the projections for last season's two MVP frontrunners, Stephen Curry and James Harden, compared to their actual 2014-15 numbers. Some things, like Harden's 3-pt % or Steph's turnovers, it got almost exactly right. Other stats, like both players' field goal attempts, were a little further off:

Per 36 Minutes Shooting
Player Type FG FGA 3P 3PA FT FTA ORB TRB AST STL TOV PTS FG% 3P% FT%
Stephen Curry Projected 8.0 17.0 3.2 7.4 3.6 4.1 0.7 4.2 7.6 1.6 3.4 22.7 .469 .437 .886
Stephen Curry Actual 9.0 18.5 3.9 8.9 4.2 4.6 0.8 4.7 8.5 2.2 3.4 26.2 .487 .443 .914
James Harden Projected 7.0 15.4 2.2 5.9 7.5 8.7 0.8 4.6 5.6 1.5 3.3 23.8 .457 .374 .861
James Harden Actual 7.8 17.8 2.5 6.7 8.6 10.0 0.9 5.5 6.8 1.9 3.9 26.8 .440 .375 .868
Provided by Basketball-Reference.com: View Original Table
Generated 10/14/2015.

 

And here are the SPS projections for Michael Jordan's last season with the Bulls, just because:

Per 36 Minutes Shooting
Rk Player Type FG FGA 3P 3PA FT FTA ORB TRB AST STL TOV PTS FG% 3P% FT%
350 Michael Jordan Projected 10.3 21.5 1.2 3.3 5.7 6.8 1.4 5.8 4.0 1.7 2.1 27.6 .481 .384 .828
351 Michael Jordan Actual 10.0 21.4 0.3 1.4 6.4 8.2 1.5 5.4 3.2 1.6 2.1 26.7 .465 .238 .784
Provided by Basketball-Reference.com: View Original Table
Generated 10/14/2015.

 

You can also see a player's 2015-16 projections at the top of their player page. Here's Carmelo Anthony's:

Per 36 Minutes Shooting
FG FGA 3P 3PA FT FTA ORB TRB AST STL BLK TOV PF PTS FG% 3P% FT% WS/48
8.9 19.9 1.8 4.8 5.1 6.2 1.8 7.0 2.9 1.0 0.5 2.4 2.6 24.7 .445 .371 .822 .139
Provided by Basketball-Reference.com: View Original Table
Generated 10/14/2015.

 

While you're getting ready for the season, don't forget that we also have the full preseason schedule with box scores, an updated free agent tracker with all of this summer's big and small player moves, a detailed injury report, and of course the most comprehensive basketball stats database on the web!

Posted in Announcement, Basketball-Reference.com, Statgeekery | Comments Off on 2015-16 Simple Projection System Projections Are Live