Wanting It More

Casual Media at its Finest

noreply@blogger.com (Matt Way) — Mon, 25 Jul 2011 07:47:00 -0500

If you learn nothing else from this blog, please learn how bad the title of this picture is. The word "mediocre" is pretty much the last thing to come to my mind when I look at those statistics. But, for ESPN, MLB Network, etc, there is a large focus on win-loss record.

The above picture is a perfect example of why NOT to focus on win-loss record. Lincecum has an ERA under 1, a K/BB rate of almost 3, and is holding opponents to an abysmal .223. But, because of the Giants' poor offense, he has only an 8-8 record. Most of that is, in all likelihood, out of Tim Lincecum's control. This is exactly why we focus on statistics like FIP for pitchers. Team defense and offense can affect how a pitcher's "traditional" stats look. It is much more important in evaluating a pitcher to look at what he CAN control.

Offensive Four Factors: Sacramento Kings

noreply@blogger.com (Matt M.) — Sun, 26 Jun 2011 11:09:00 -0500

Coming off of Tyreke Evans' impressive rookie year and the selection of DeMarcus Cousins in the 2010 NBA Entry Draft, many figured the Kings had the potential to take a step forward towards relevance. Unfortunately a number of injuries, the incompetence of Paul Westphal, and the severe regression (more on this later) of Tyreke Evans, made for another disappointing season.

Sacramento Kings

Offensive Rating - 103.5 (25th)

Effective Field Goal Percentage - 48% (26th)
Turnover Percentage - 14.4% (26th)
Offensive Rebounding Percentage - 29.9% (1st)
Free Throw Ratio - 20.8% (28th)

The first thing that jumps out at me is the huge gap in their performance across the various metrics. Sacramento was 1st in the league in Offensive Rebounding but in the bottom 5 in everything else. This has been said for some of the league's other poor offenses, but Sacramento's ability to crash the offensive glass saved them from a historically bad offense.

Much like the teams in the bottom 5, Sacramento's main problems came from their inability to score the ball efficiently. Sacramento's two highest usage players were Tyreke Evans and DeMarcus Cousins. Neither of these two players scored the ball well. The two players had the same eFG of 43.2%, a terrible number for anybody, but especially for players tasked with leading an offense. Marcus Thornton was quite good offensively in his time with the team, but that was only in 1000 minutes, so his relative efficiency was but a drop in the bucket. Beno Udrih also had an impressive year offensively, but his usage was incredibly low.

This leaves Evans and Cousins as the key culprits in both shooting the ball and protecting the ball. Cousins featured an 18.5% turnover percentage, and Evans a 14.9% mark. This did well to seal the team's fate as a group that struggled in protecting the basketball, and low turnover numbers from players like Beno Udrih, Francisco Garcia, Marcus Thornton, Omri Casspi and Carl Landry was not able to offset the damage done by Evans and Cousins.

The team's only bright spot offensively was its ability to crash the offensive glass. Samuel Dalembert, Carl Landry, and DeMarcus Cousins (his only bright spot) all had double digit OREB%. As I mentioned earlier, this saved them from a historically bad offense.

Getting to the line with any consistency was also a major issue for the Kings. DeMarcus Cousins led the way with 5 trips to the line per game. Their other leaders were Carl Landry (4.1 attempts), Marcus Thornton (4.4 attempts) and Tyreke Evans (4.7 attempts). Other than these three, no rotation player got to the line more than 3 times every game.

Sacramento experienced a perfect storm of offensive incompetence. The combination of poor shooting, a high turnover rate, and an inability to get to the line cemented their fate as offensive cellar dwellers. Even with the selection of Jimmer Fredette and the acquisition of John Salmons, this team will need massive improvements from both Evans and Cousins to show any sign of respectability.

Offensive Four Factors: Charlotte Bobcats

noreply@blogger.com (Matt M.) — Mon, 20 Jun 2011 17:30:00 -0500

Coming off the best season in franchise history which saw Charlotte make the playoffs for the first time, there was some optimism that the team could build off of that experience and slowly position itself as a sleeper in the East. This never materialized, as 2010-11 saw Charlotte miss the playoffs yet again. This was due in large part to an anemic offense. We'll attempt to explain why.

Charlotte Bobcats

Offensive Rating - 103.4 (26th)

Offensive Four Factors Performance:

eFG: .482 (25th)
TOV: .141 (24th)
OREB: .257 (18th)
FTA/FGA: .237 (7th)

As is the case with the league's other bad offenses, the problem begins with shooting. It is rare to find a team perform shoot poorly yet still do well offensively. Unfortunately for Charlotte, they matched up poor shooting with a high turnover rate and mediocre offensive rebounding. Let's take a look at why they struggled putting the ball in the hoop.

If we look at a combination of USG% and Minutes Played, the 4 players who had the biggest impact on their season were Boris Diaw, DJ Augustin, Stephen Jackson, and Gerald Wallace. Of those 4, Diaw had the lowest USG% (only 16.8% - meaning he was rarely the focal point). Neither of the 3 remaining players did well in terms of efficiency. Augustin had an eFG of 47.4%, Jackson's was 46.8%, and Wallace's was 46.3%. All three of these players had similar profiles in terms of their shot attempts - all three were poor 2 point shooters on varying degrees of volume and all were decent 3 point shooters on varying degrees of volume.

Lacking any credible inside threat allowed defenses to adjust accordingly, and the possessions that didn't end in a turnover typically ended in a low efficiency shot taken by one of those three players.

Charlotte was also poor at limiting turnovers. The biggest culprit here was Stephen Jackson, who had a TOV of nearly 15% compared to the 11.4% league average for small forwards. The big issue here is not just his turnovers in a vacuum, but that he was by far Charlotte's number one offensive option. The other culprit of Boris Diaw. Diaw will always have a higher TOV than most bigs given his style of play, but that is no excuse. Teams that have difficulty shooting the ball need to protect the ball incredibly well to have any success, and having two players who are some of the worst at their positions in terms of protecting the ball makes matters worse.

Charlotte's offensive rebounding was a tinge below average. I suspect that part of this was due to the inexcusable decision to limit Tyrus Thomas' minutes early in the season (and then, of course, his season ending injury). The other factor here was Boris Diaw (again). Diaw put up a putrid 4.5% offensive rebounding rate, and given that he played all of his minutes at either the 4 or the 5, we can quickly pick up why Charlotte struggled on the offensive glass.

Getting to the rack was the only bright spot in Charlotte's offensive resume this past year. However, when we look at the free throw rates of some of their players, we see some confusion, as outside of Gerald Wallace's (1872 minutes) 5.6 attempts/game and Kwame Brown's (1714 minutes) 4.9 attempts/game, the only player who got to the line with any frequency was Stephen Jackson at 4.6 attempts/game. This is where their inability to protect the ball coupled with their inability to grab offensive rebounds hurt them. Even though they got to the line a good deal as a proportion of their total field goal attempts, their raw free throw attempts were low, simply because they had fewer possessions per game.

Offensive Four Factors: New Jersey Nets

noreply@blogger.com (Matt M.) — Sun, 19 Jun 2011 11:41:00 -0500

Our series will continue with the New Jersey Nets, a team who was unable to add a marquee free agent despite their imminent move to Brooklyn. Adding Deron Williams was a minor coup, although he did little in the way of bringing up some of their horrible offensive numbers.

New Jersey Nets

Offensive Rating - 103.1 (27th)

Offensive Four Factors Performance

eFG%: 47.4% (27th)
TOV: 13.4% (t15th)
OREB: 26.1% (15th)
FTA/FGA: .215 (24th)

Once again, we see one of the league's worst offenses driven by their inability to score efficiently. Despite being more of a jumpshooter, Brook Lopez was not the problem here. The problem was with the point guards and the wings. Devin Harris had an eFG of 45.2%, Jordan Farmar had an eFG of 47.5%, Travis Outlaw had an eFG of 42.7%, and Deron Williams had a putrid eFG of 39.6% in his short stint with the Nets.

However, I would expect there to be a decent bounceback next year, as Deron Williams will certainly recover (career eFG of 50.6%), and as long as he stays healthy, he should figure to cut deeply into the minutes that Jordan Farmar gets. Of course, Devin Harris is no longer with the team.

Turnovers were once again driven by the guards - as Lopez and Humphries did well in protecting the ball. The problem was Jordan Farmar's 16.7% turnover rate and Devin Harris' 17.5% turnover rate. Deron Williams is not as good at protecting the ball as other elite point guards, but his shot creation and playmaking ability should ostensibly help going forward.

Offensive rebounding is the area where Brook Lopez's perimeter tendencies really come into play. He had a putrid 7.8% offensive rebounding rate. The Nets' league average OREB rate was driven by Kris Humphries and Derrick Favors (who is no longer with the team).

The Nets were towards the bottom of the league in terms of getting to the line. This seemingly was driven by having wings who were primarily spot-up shooters. Deron Williams will help, but the combination of a perimeter-oriented big + jump shooting wings will likely keep them at the bottom of the league in their ability to get to the line. This isn't necessarily a big deal if the team can improve offensive performance in other areas, but unless they make another move this summer, I don't see the possibility of appreciable offensive improvement.

NBA Finals: 4th Quarter Analysis

noreply@blogger.com (Matt Way) — Sun, 19 Jun 2011 10:38:00 -0500

As is often the case in the media, the 4th quarters of the NBA Finals were analyzed over and over again by ESPN and ABC. In this year's case, that emphasis on 4th quarter analysis was well placed. Every one of the six games was in doubt come the 4th quarter. So why did Dallas perform so well in the 4th quarters? We at Wanting It More have collected all of the 2011 NBA Finals 4th quarter statistics to give you a better idea.

The main focus of the media in 4th quarter analysis was on LeBron James. It appears the LeBron criticism was well founded. Not only did LeBron have the 7th highest point per possession rate among players with a significant amount of possessions used, LeBron used less possessions offensively than Dwyane Wade, Chris Bosh, Dirk Nowitzki, and Jason Terry. He used less than half the amount of possessions Wade and Dirk used, and he barely used more possessions than JJ Barea, Mario Chalmers, and Udonis Haslem. Considering how close the games in the Finals were in the 4th quarter, it is difficult to come up with a scenario in which the Heat could win the series with LeBron using so few possessions. Now, he did have a lot more assists than even the 2nd closest player, but even then, he used far less possessions than Dirk and Wade, still less than Terry, and the same amount of possessions as Bosh. And he did all of this with having the most 4th quarter turnovers in the series (tied with Dirk).

The numbers confirm that Dirk was dominant. Dirk had an excellent 57 true shooting percentage on an inordinate amount of possessions. His turnover rate was respectable given his usage (13%), but most importantly, he was by far the most aggressive player in the 4th quarter of games, shooting twice as many free throws as anyone (other than Chris Bosh- 24 to 13).

The player that quietly had excellent 4th quarter performances is Tyson Chandler. Chandler had NINE offensive rebounds in the 4th quarter of games. The Mavericks had 13 "extra" possessions in the fourth quarter of those games, nearly all of those being a result of Chandler's excellent offensive rebounding. Given the close nature of these games, it wouldn't be a stretch at all to consider Chandler the difference maker.

Between LeBron's passiveness, Bosh's poor efficiency, Dirk's outstanding offense, and Chandler's offensive rebounding, it is clear why the Mavericks dominated the 4th quarter of the Finals. And it's important to note that it all showed up in the box score.

Offensive Four Factors: Washington Wizards

noreply@blogger.com (Matt M.) — Sun, 12 Jun 2011 17:57:00 -0500

Much that was said about Washington throughout the season focused on off the court issues. Whether it was shenanigans with Gilbert Arenas or talk about the deadline deal with Magic, little was actually said about the team's play, which makes sense, considering this was one of the worst offenses in the league.

Washington Wizards

Offensive Rating - 102.4 (28th)

Offensive Four Factors Performance

eFG%: 47.1% (29th)
TOV: 13.9% (23rd)
OREB: 28% (9th)
FTA/FGA: (23rd)

Much like the Bucks and Cavaliers, Washington's offensive struggles were primarily caused by their inability to make shots. Washington gave 2000+ minutes to John Wall, Javale McGee, Andray Blatche and Nick Young. Young was the only player with high usage and a somewhat decent eFG% at 49.7%. This was mostly driven by shooting 39% from 3 at a pretty high volume (4.2 attempts/game). Other than Young, Washington's high usage players all were abysmal in terms of shooting - below are the shooting numbers for John Wall and Andray Blatche.

John Wall - eFG% 42.7, TS% .494
Andray Blatche - eFG% 44.7, TS% .497

Both of these players were called on often and neither performed with any efficiency.

Washington's turnover problems were primarily the result of 3 players: John Wall, Andray Blatche, and Kirk Hinrich. Hinrich was only with the team for 48 games and didn't have too high of a usage but still had a pretty terrible year in terms of protecting the ball. Blatche was Washington's highest usage player and had turnover numbers right in line with Washington's as a team. The worst culprit, however, was John Wall. Wall turned the ball over an estimated 18.6 times in 100 plays. This figure is one of the worst in the league, especially for a guard, and especially for a player that played such a large role in his team's offense.

Washington's offensive rebounding was quite good, driven largely by the play of garbageman Javale McGee. The last factor was most in line with Washington's shooting and ballhandling woes, as their team FTA/FGA was 23rd in the league. John Wall was the only Wizard who got to the line with any frequency. This makes sense given the huge gap in his eFG% and his TS%. The inability for any other Wizard to get to the line also makes sense, given their tendency to take lots of jumpshots. Unfortunately for Washington, few of these shots went in.

Offensive Four Factors: Cleveland Cavaliers

noreply@blogger.com (Matt M.) — Sun, 12 Jun 2011 12:17:00 -0500

Offensive Four Factors - Cleveland Cavaliers

Our next post in this series will highlight the offensive of the NBA's second worst offense, the Cleveland Cavaliers. Marred by the loss of LeBron James, most NBA prognosticators felt that a serious drop in offensive efficiency was in order - it's safe to say these people were correct. The Cavaliers offense scored barely over one point per possession. Let's figure out why.

Cleveland Cavaliers

Offensive Rating - 102.2 (29th)

Offensive Four Factors Performance

eFG%: 47.2% (28th)
TOV: 13.4% (15th)
OREB: 24.2% (25th)
FTA/FGA: .233 (11th)

The two things that jump right out were their poor shooting and poor rebounding numbers. The 5 players who played the heaviest minutes were JJ Hickson, Ramon Sessions, Anthony Parker, Daniel Gibson and Antwan Jamison. None of these players are particularly great shooters, especially Sessions, Hickson, and Jamison, the three players who took on the heaviest offensive roles.

The same holds true for rebounding. Outside of Anderson Varejao, Cleveland had little in the way of competent big-man play. There are teams who are able to offset the handicap of poor shooting with above average offensive rebounding. Cleveland was not one of those teams, as they performed well enough in the other 2 factors.

Much like Milwaukee's low turnover numbers helped save them from a historically bad offense, Cleveland's ability to get to the line with a decent amount of frequency coupled with their league average ball protection helped save them from being a complete and utter laughing stock.

Offensive Four Factors: Milwaukee Bucks

noreply@blogger.com (Matt M.) — Sun, 12 Jun 2011 11:26:00 -0500

This is our first post in a series what will chronicle the offensive performance of each team in the 2010-2011 NBA season. We will perform our analysis based on the four factors as detailed by Dean Oliver and distilled in our previous post.

Milwaukee Bucks

Offensive Rating - 101.6 (30th)

Four Factors Performance

effective Field Goal % - 46.7% (30th)
Turnover % - 13% (9th)
Offensive Rebounding % - 24.7% (22nd)
Free Throw/Field Goal Attempts Ratio - 21.7% (21st)

The explanation for Milwaukee's putrid offensive performance is pretty simple - they didn't make shots. While a league worst Effective Field Goal % provides the bulk of the explanation, we do need to dig a little bit to gain some context. Although there's a relationship between eFG% and ORating, there are some cases this year where teams have made up for poor shooting (Portland, for one).

The problem with Milwaukee was that they complemented their terrible shooting with poor rebounding and penetration numbers. They ranked 22nd in Offensive Rebounding % and 21st in Free Throw Attempts as a percentage of Field Goal Attempts. Both of these events can help an offense that cannot shoot well. Unfortunately for Milwaukee, they did neither of these well enough to offset their shooting woes.

The bright side is that they protected the ball very well, having the 9th lowest turnover % in the league. Possessions are paramount for any team, but the importance of a single possession is magnified when that team struggles to the degree that Milwaukee struggled. Luckily for them they were able to protect the ball well enough. Ballhandling more in line with the rest of their offensive numbers had the potential to lead to a historically bad offense.

The Offensive Four Factors

noreply@blogger.com (Matt M.) — Tue, 31 May 2011 23:58:00 -0500

The Four Factors are what Dean Oliver identified in Basketball on Paper as the key to winning basketball games. Over the next series of posts we will break down each team's production both offensively and defensively according to these metrics. We will begin with offense.

The Four Factors are:

Shooting (eFG%) **Insert link to eFG% post**

Rebounding (OREB%)

Ball-handling (TOV%)

Drawing Fouls (FTA/FGA)

We will look at each team on a case-by-case basis, providing a brief explanation of their relative performance in each factor. We'll start at the bottom of the league with the Milwaukee Bucks and work our way to the top of the league with the Denver Nuggets. A new team will be profiled each day. Each team will be looked at in great detail. The goal is to provide context for each team's performance on the court.

Dispelling Myths: LeBron James

noreply@blogger.com (Matt Way) — Fri, 27 May 2011 17:57:00 -0500

At Wanting It More, our main goal is to make people think about sports differently than what is traditional based on media perception. In this series, we will focus on specific players and our individual opinions on them based on certain criteria. This first post focuses on one of the most controversial figures in sports currently.

I'm a huge fan of Detroit sports teams. It comes with the territory of growing up and living in Michigan. I'm a big Pistons fan, but growing up just after the Bad Boys era, my NBA interest was peaked by the Bad Boys primary antagonist: Michael Jordan.

I will confess... I grew up a Bulls fan. And Michael Jordan's greatness had everything to do with it. The Pistons weren't any good and Michael Jordan was simply the man. He's everything you wanted being a big sports fan. He was ultra-competitive, great, and had an unmatched desire to win. He's a big part of the NBA fan I grew up to become.

To me, LeBron James is now what Michael Jordan used to be in a lot of ways. Most importantly, he's simply the best basketball player in the world. And, in my opinion, he has been for quite some time. LeBron soul crushed the Pistons in the 2007 NBA Playoffs. Specifically, he dominated in Game 5 which will forever be one of the finest individual game playoff performances you will ever see. As a Pistons fan, I was crushed. As a basketball fan, I was amazed. I understood that, despite my team allegiances, we were witnessing the beginning of something very special. LeBron's absolute refusal to lose that game was reminiscent of that player I grew up loving.

In the next few years, my love for LeBron as a player only grew. He dominated games on a level that you just do not see very often. It was clear to me that I was watching a once in a generation type of player. He was absolutely unstoppable driving to the basket and he had an innate ability to finish around the rim whenever he got there (which was seemingly every possession).

LeBron's ability to shoulder a huge load offensively (usage) at such a high efficiency (TS%) with a low turnover rate is something that we haven't seen from a wing player since His Airness. Combine that with his incredible defense, and the Michael Jordan comparisons should not be surprising.

Fast forward to last summer, and LeBron saw a lot of criticism for the way he handled The Decision. Plenty of that criticism was deserved. He probably should have told the Cavs ahead of time. He probably should have not made such a spectacle of the decision, even though it generated millions of dollars for charity. What I don't understand is the criticisms directed at his "taking the easy way out."

LeBron gave Cleveland everything he had. He didn't complain, he just played amazing basketball for seven years. Cleveland just failed to put the necessary pieces around him. The easy way out would have been to stay with his hometown team and not have as much pressure around him. But he took a risk. He chose to team up with Dwyane Wade and Chris Bosh, fully knowing that the Miami Heat would have incredible expectations from year one. But LeBron wanted to win. He wanted to cement his place in history.

With his performance in the last two series, I believe that he's well on his way. LeBron's performance against the Chicago Bulls was nothing short of spectacular. He took on the responsibility of guarding the reigning MVP while still shouldering a heavy load offensively. LeBron's effort on both sides of the ball was reminiscent of another player that I grew up loving so much. And he did it seemingly effortlessly. While the Bulls were grabbing their shorts late in games, LeBron looked like he did when the games had begun.

Today, LeBron James is just a series away from winning his first championship at age 26. And unlike LeBron's first attempt in 2007, his team is a decided favorite in the NBA Finals. Michael Jordan won his first championship at age 27. And given the Heat's younger core and the fact that they've found a way to co-exist as the season has gone along, it shouldn't surprise anyone if the Heat rattle off a lot of championships in the next 6 years.

Will LeBron James ever surpass Michael Jordan? Maybe not. Scottie Pippen doesn't necessarily agree with that sentiment, though. But, given his production and career arc, LeBron James is well on his way to doing something special and cementing his name forever in the record books among the all-time greats. And, at some point, people are going to have to give him the credit he so greatly deserves.

Game Review: Heat at Bulls- Game 1

noreply@blogger.com (Matt Way) — Mon, 16 May 2011 18:47:00 -0500

The much anticipated Game 1 of the Eastern Conference Finals turned into a blowout. Raise your hand if you had the Heat getting blown out with Chris Bosh scoring 30 points. Why were the Bulls so successful? Let's take a closer look at the box score.

First, laying out the facts:

Possessions (FGA + 0.5FTA + TO - OREB)- Heat 85, Bulls 87

Note: Individual possessions = FGA + 0.5FTA + TO - 1/3FGmisses

Points- Heat 82, Bulls 103

Points per possession- Heat 0.96, Bulls 1.18

Offensive rebounds- Heat 6, Bulls 19

Turnovers- Heat 16, Bulls 9

The Bulls' advantage in offensive rebounds and turnovers resulted in TWENTY "extra" possessions in Game 1. To put this in perspective, the difference between the best and worst offensive rebounding teams this year was about 5.5 per game. The difference between the best and worst turnover teams this year was about 4 per game. So if you take the best offensive rebounding team, combine it with the best team at limiting turnovers and combined the worst two teams in those categories, the difference would be about 9.5 "extra" possessions per game. The Bulls had more than double that many "extra" possessions against the Heat.

Multiply those 20 extra possessions by the Bulls 1.18 points per possession and you get 23.6 points. The Bulls won by 21, so it's quite easy to see why they won by the disparity that they did.

A lot of credit in Game 1 should go to Joakim Noah. He was poor shooting the ball, but managed to grab 8 offensive rebounds and only turned it over once. Luol Deng also quietly had a great game as usual. He used about 15 possessions while scoring 21 points. Additionally, he grabbed 2 offensive rebounds, 5 defensive rebonds, and had 4 steals. He drew the defensive assignment of LeBron James and performed admirably.

LeBron used approximately 18 possessions scoring only 15 points. Similarly, Dwyane Wade used approximately 20 possessions scoring only 18 points. Collectively, they accounted for only one offensive rebound (by LeBron).

Simply put, the Heat have no chance if LeBron and Wade account for less than one point per possession in this series. Add on top of that twenty extra possessions for the Bulls, and it shouldn't be surprising that the Bulls ran away with Game 1.

Can we expect this to continue the entire series?

The Bulls are certainly a great offensive rebounding team, but the Heat were the fourth best team in the league this year in terms of defensive rebounding percentage. The Heat and Bulls are fairly even in terms of turnover percentage. So, the short answer is no. We shouldn't expect that to happen the entire series. Though, if Tom Thibodeau saw something on tape that he thinks he can exploit, maybe it will continue.

It may be more likely, believe it or not, to expect LeBron and Wade to struggle offensively in this series. The Bulls' team defense is outstanding, and they did an excellent job keeping them out of the paint in Game 1. If you watch closely, every time LeBron and Wade start to penetrate, the Bulls put up a wall of defenders close to 12 feet out. Based on the Bulls' philosophy in Game 1, LeBron and Wade may have to shoot very well in order for Miami to win the series.

Podcast: NBA Eastern Conference Finals Preview

noreply@blogger.com (Matt Way) — Sat, 14 May 2011 18:40:00 -0500

Matt and Matt discuss the Heat/Bulls match-up, give their predictions, and briefly discuss Game 7 between the Grizzlies and Thunder.

Daily Dictionary: Ground Ball%, Fly Ball%, Line Drive%

noreply@blogger.com (Matt Way) — Thu, 12 May 2011 13:37:00 -0500

Daily Dictionary: Ground Ball%, Fly Ball%, Line Drive% (GB%, FB%, LD%)

The concept behind these statistics is extremely simple. Each three of these statistic measures the percentage of time a batted ball results in the particular outcome. While simple, these statistics are exceedingly important. We discussed previously how BABIP is a good predictor for hitters in terms of how lucky a particular hitter has been over a certain sample size. Unlike pitchers, though, hitters have a certain amount of control over their BABIP. In that sense, it is better to compare a hitter's current BABIP to his career BABIP. These peripheral statistics help us show why certain hitters have higher BABIP than others, and can help us predict even better if a hitter has been especially lucky in a particular year.

Line drives result in hits 74% of the time.

Ground balls result in hits 28% of the time.

Fly balls result in hits 21% of the time.

Accordingly, a hitter with a higher line drive percentage is going to have a higher than normal BABIP. Hitters with high fly ball percentages are going to have a lower than normal BABIP. Ground ball percentages are going to result in different outcomes for different players because of their individual speeds.

One hitter that has an abnormally high BABIP is Ichiro Suzuki with a career .356. His peripherals are as follows:

LD%: 20.3

GB%: 55.7

FB%: 24.0

Now, if we assign the percentage of the time those plays result in hits on average, we get:

(.203*.74)+(.557*.28)+(.24*.21) = .356

Now, those numbers aren't going to work out that nicely every time, but it will give you a general idea of how and why a hitter's BABIP is what it is. In Ichiro's case, his BABIP is extremely high because he hits a lot more ground balls than fly balls. Most hitters hit more fly balls than ground balls. Curtis Granderson is one of those guys:

LD%: 20.4

GB%: 35.7

FB%: 43.9

So, Granderson hits about 20% less ground balls and 20% more fly balls than Ichiro. Because Granderson hits a less optimal result more often, his career BABIP is significantly lower at the rate of a career .313.

So what does this all mean? When we're looking at a player who has a significantly higher or lower BABIP than his career BABIP, we should also look at his LD%, GB%, and FB% to see how those numbers correspond with his career. If a player has a higher BABIP than usual and his peripheral statistics are similar to his career averages, he might just be getting lucky. If his fly ball rate is higher than usual, there's a good chance he's getting lucky with regard to fly balls resulting in home runs. If his line drive rate is higher than usual, he's probably just hitting the ball better.

Daily Dictionary: Effective Field Goal Percentage

noreply@blogger.com (Matt M.) — Tue, 10 May 2011 14:10:00 -0500

Daily Dictionary: Effective Field Goal Percentage (eFG%)

The idea behind eFG% is simple - 3 point field goals are worth 50% more than 2 point field goals. The calculation is also simple: eFG% = (FG + 0.5 * 3P FG) / FGA

Today's post will focus solely on eFG% in the context of individual players. Over the next few days we'll talk about team eFG% and how we can use eFG% against to analyze team defense.

To help illustrate this point, we've included a top 12 list.

1. Nene Hilario - .615
2. Dwight Howard - .593
3. Arron Afflalo - .581
4. Richard Jefferson - .579
5. Ray Allen - .577
6. Emeka Ofakor - .573
7. Lamar Odom - .568
8. Marcin Gortat - .561
9. Jared Dudley - .560
10. Al Horford - .558
11. Ty Lawson - .553
12t. Stephen Curry - .551
12t. Paul Pierce - .551
12t. Greg Monroe - .551

The styles of play represented by this list are pretty diverse - we have inside scorers in Nene, Dwight, Okafor, Gortat, Horford and Monroe. Interestingly enough, these have the 6 highest field goal percentages in the league. This is a pretty simple connection to make, as none of these players take 3 pointers with any frequency whatsoever.

Outside of Lamar Odom, the remaining players on this list are all guys who shoot a high number of threes at a high percentage.

Afflalo - 42.3% on 3.6 attempts/game
Jefferson - 44% on 3.8 attempts/game
Allen - 44.4% on 4.7 attempts/game
Dudley - 41.5% on 3.1 attempts/game
Lawson - 40.4% on 2.1 attempts/game
Curry - 44.2% on 4.6 attempts/game
Pierce - 37.4% on 3.7 attempts/game

Like True Shooting, eFG% attempts to compare the offensive efficiencies of different types of players, and in doing so, highlights the deficiencies of traditional FG%.

Daily Dictionary: wOBA

noreply@blogger.com (Matt Way) — Thu, 5 May 2011 14:52:00 -0500

wOBA- weighted On-Base Average

wOBA = ((.72*NIBB)+(.75*HBP)+(.9*1B)+(.92*RBOE)+(1.24*2B)+(1.56*3B)+(1.95*HR))/AB

NIBB = Non-Intentional Bases on Balls

RBOE = Reached Base on Error

Now, this calculation may look daunting but it's really rather simple. wOBA is an improvement on OBP (and OPS accordingly) based on linear weights. The coefficients for the respective hits/at-bat results are simply a run value of the particular event. So when a player hits a single, it results in, on average, 0.9 runs. Obviously, a lot of the time a single isn't going to bring in any runs. But, when you have men on second and third, a single will often bring in two runs. These coefficients are derived from historical numbers, so they aren't just theoretical... they're what actually happens. A home run results in, on average, 1.95 runs. That just means that, on average, home runs are hit when one runner is on base. The first run is result of the hitter and the 0.95 is from the average runners on-base at the time of the home run.

wOBA is basically the best hitting statistic you will find. It is descriptive rather than predictive. This is an important distinction. BABIP, which we discussed previously, will give you good predictions on what may happen in the future. wOBA is simply a description of what has happened. It is important not to confuse the two when talking about different types of analysis.

Top 5 hitters in wOBA in 2010:

1. Josh Hamilton- .447

2. Joey Votto- .439

3. Miguel Cabrera- .429

4. Jose Bautista- .422

5. Albert Pujols- .420

As you can see, wOBA is an extremely accurate measure of who the top hitters were in 2010.

Daily Dictionary: Defensive Rating

noreply@blogger.com (Matt M.) — Tue, 3 May 2011 13:41:00 -0500

Defensive Rating

This post will make the jump back to team-level analysis. Defensive Rating, while flawed, offers a more complete understanding of a team's overall defensive efficiency. The statistic is extremely simple - it is merely a measure of how many points a team will allow per 100 possessions.

Below are the teams with the 5 best and 5 worst Defensive Ratings:

1t. Chicago Bulls - 100.3
1t. Boston Celtics - 100.3
3. Orlando Magic - 101.8
4. Milwaukee Bucks - 102.5
5. Miami Heat - 103.5

26. Golden State Warriors - 110.7
27. Minnesota Timberwolves - 111.1
28. Detroit Pistons - 111.7
29. Cleveland Cavaliers - 111.8
30. Toronto Raptors - 112.7

Defensive Rating is not adjusted for pace, which limits its utility. Despite this limitation, it is still a superior metric for team level defense, as by converting the unit to a point per possession basis, it automatically adjusts for the frequency a team will allow 3 point shots and free throws.

Daily Dictionary: % Assisted

noreply@blogger.com (Matt M.) — Fri, 29 Apr 2011 13:56:00 -0500

Daily Dictionary - % Assisted

Today's post will take the scope of our analysis away from the world of efficiency and pace and into the world of shot creation. Shot creation is one of the most important traits of a successful basketball player. The statistic we can use as a proxy for shot creation is % assisted. This is a statistic that can be found on HoopData, in the scoring section of their Player Statistics page.

% Assisted - >40 games, >25mpg

We have applied the same Games Played at Minutes/Game boundaries but have also filtered by position.

Point Guards

Shooting Guards

Small Forwards

Power Forwards

Centers

League Average % Assisted:

Point Guards - 34.2%
Shooting Guards - 56%
Small Forwards - 62.5%
Power Forwards - 61.4%
Centers - 63.2%

Looking at the league averages by position, the trend is pretty obvious and corroborates with intuitive viewing of the game. Players who are most likely to create their own shot (have a low % assisted) are point guards and ball dominant wings. Players who are less likely to create their own shots (have a high % assisted) are spot-up shooters and players who take most of their shots close to the basket.

Another thing to note is the general fungibility among players who cannot create their own shot. Of course this doesn't hold for the elite bigs and the players who elite shooters, but in most cases, the key differentiating factor is the ability to get your own shot up at any point in the game and during any point of a possession.

Daily Dictionary: OPS

noreply@blogger.com (Matt Way) — Wed, 27 Apr 2011 11:02:00 -0500

OPS- On-base percentage (OBP) Plus (+) Slugging percentage (SLG)

OBP = (hits + walks + hit by pitch)/(at-bats + walks + sacrifice flies + hit by pitch)

SLG = (total bases)/(at-bats)

OPS improves on the standard batting average statistic in several ways. First, batting average fails to take into account walks and being hit by a pitch. On-base percentage corrects that omission of batting average. Second, batting average fails to take into account power. Obviously, if two guys hit for the same batting average and walk the same amount of times, the guy who hits for more power is going to be a more valuable hitter. Slugging percentage corrects this omission of batting average. Improving on batting average's two primary faults, OPS is one of the best, easiest to find advanced statistic for evaluating hitters. Nearly any website devoted to baseball will have OPS as a searchable statistic. Even ESPN.com now has OPS numbers in a player's statistics page. If you can't find OPS, you can calculate it simply by adding on-base percentage and slugging percentage.

2010 leaders in OPS:

1. Josh Hamilton- 1.044

2. Miguel Cabrera- 1.042

3. Joey Votto- 1.024

4. Albert Pujols- 1.011

5. Jose Bautista- 0.995

6. Paul Konerko- 0.977

7. Carlos Gonzalez- 0.974

8. Troy Tulowitzki- 0.949

9. Matt Holliday- 0.922

10. Jayson Werth- 0.921

League average OPS is 0.728

Daily Dictionary: Batting Average on Balls In Play

noreply@blogger.com (Matt Way) — Tue, 26 Apr 2011 00:34:00 -0500

Batting Average on Balls in Play (BABIP)

BABIP = ((hits - home runs)/(at bats - strikeouts - home runs + sacrifice flies))

BABIP measures the percentage of at-bats that hitters get or pitchers give up when the ball is put in play. We're starting the baseball analysis here because it is an important part of baseball analysis. Hitters and pitchers both have relatively little control of what happens once the ball is put in play (we'll look at this below). BABIP basically measures how lucky or unlucky a hitter or pitcher is over a smaller sample size. BABIP is proven, over a long example, to be very consistent among all pitchers. BABIP varies a little more among hitters, mostly among fast hitters who can beat out more infield hits.

Roy Halladay career BABIP: .292

Roger Clemens career BABIP: .284

Randy Wolf career BABIP: .284

Kevin Appier career BABIP: .284

So, Kevin Appier and Randy Wolf were equally as good (or better) as/than Clemens and Halladay at batting average on balls hitters put in play against them. Like it was said above, that's because pitchers don't have any control of what happens when the ball is put in play. Bad bounces happen. Some defenses are better than others. What pitchers can control is walking and striking out hitters. That is what separates Clemens and Halladay from the rest of the mediocre pitchers.

Barry Bonds career BABIP: .285

Travis Fryman career BABIP: .313

Now, as a lifelong Tigers fan, Travis Fryman was one of my favorite players growing up. But he ain't Barry Bonds. Yet playing 13 seasons and accumulating over 7200 at-bats, Fryman's BABIP is significantly higher than that of the best hitter in baseball history. Why was Barry Bonds so much better? Again, he struck out less and he drew a ton more walks (as well as hitting for incredible power... more on that in a future post).

BABIP can also be used in the context of an individual player. If you want to find out if a particular player is performing at a higher/lower level than usual or just getting lucky/unlucky, compare their BABIP in the current season to their career average. This can also be used to project whether a player will have a "bounce back" year. We'll check out some examples of this in a future post.

Daily Dictionary: Pace

noreply@blogger.com (Matt M.) — Mon, 25 Apr 2011 13:10:00 -0500

Pace

48*((Team Possession + Opponent Possession) / (2*(Team Minutes/5)))

Pace is another team-level statistic that cannot be gleaned by simply viewing a boxscore. Pace is important to note during an analysis, because it can sometimes skew counting stats such as Points, Rebounds, Assists. Pace roughly estimates the number of possessions used by a team in a given game.

Here are the Top 5 and Bottom 5 teams in pace:

1. Minnesota - 96.5
2t. New York - 95.6
2t. Denver - 95.6
4. Sacramento - 95.2
5. Golden State - 94.8

26. Charlotte - 89.6
27. Atlanta - 89.3
28. Detroit - 89.2
29. New Orleans - 88.7
30. Portland - 87.9

It goes without saying that when holding usage constant, players will use a higher number of raw possessions when in a high pace offense. This, of course, will lead to more opportunities to accumulate bulk statistics. Later on in the week I will address in greater detail the impact of pace.

Daily Dictionary: Possessions

noreply@blogger.com (Matt M.) — Thu, 21 Apr 2011 16:14:00 -0500

Daily Dictionary: Possessions

Team: FGA + .44FTA + TOV - OREB
Individual: FGA + .44FTA + TOV - 1/3*Missed FG (included because 1/3 of FGA result in an offensive rebound)

The goal of these first few dictionary posts are to introduce some of the major concepts that will be applied in our game reviews. We started with a few individual level statistics that more completely measure a players offensive contribution. True Shooting and Usage are extremely valuable tools, but they fail to measure the impact of Offensive Rebounding and Turnovers on team success.

One key point to understand is that teams will generally have an extremely similar number of possessions in a given game (+/- 1 or 2). This places a premium on the ability to score efficiently, crash the offensive glass, and limit turnovers. Matt W. will have a post up shortly on the impact of offensive rebounds and turnovers, so I'll instead focus on scoring.

As I referenced yesterday, many fans tend to overrate pure bulk scoring, and in doing so, they neglect to take into consideration how many possessions are used to get these points.

Player A: 25.3 Points/Game
Player B: 26.7 Points/Game

Player A Possessions Used: 20 + .44(7.1) + 3 - .33(11) = 26.124 - 3.63 = 22.494 Possessions/Game
Player B Possessions Used: 17.5 + .44(7.8) + 3.3 -.33(8.6) = 24.232 - 2.838 = 21.394 Possessions/Game

Player A Points Per Possession = 25.3/22.494 = 1.12
Player B Points Per Possession = 26.7/21.394 = 1.25

Player A is Kobe Bryant, Player B is LeBron James

All of this information is readily available in the boxscore, and can be done for single games or for an entire season (or career). These simple calculations provide a deeper insight into the offensive contributions of a specific player.

Game Review: Pacers at Bulls- Game 2

noreply@blogger.com (Matt Way) — Thu, 21 Apr 2011 14:30:00 -0500

On Monday, the Bulls held off the Pacers to win Game 2 by the score of 96-90. The outcome was not unexpected. But, why did it happen?

First, by way of introduction, here is the ESPN box score. Now, if you're looking for something a bit more advanced, here is the Hoopdata box score.

Looking through the regular box score, one guy stands out as the difference: Derrick Rose. Rose is the guy that will generally stand out if you're looking at Bulls games. But, was he most responsible the the Bulls' Game 2 win?

Rose had 36 points, 8 rebounds, 6 assists, and 6 turnovers. He scored his 36 points on 58.6% true shooting. That can be calculated with the regular box score numbers using the formula found in the True Shooting Percentage Daily Dictionary post. Luckily, though, Hoopdata does the math for us! Rose's true shooting was very good in this game, but let's look at his scoring on another level: a point per possession basis.

Possessions can be estimated pretty well through this formula: FGA + .5FTA + TOV - (1/3)missedFGA. The way possessions are currently calculated, offensive rebounds extend a possession rather than creating a new one. The 1/3 coefficient is the approximate amount of times an offensive rebound results from a miss. So, for every three shots a player takes, approximately one of those shots results in a continued possession.

Using that formula, Derrick Rose used approximately 33 possessions while scoring his 36 points leading to a point per possession rate of 1.1 (36/33). That's not bad, but to determine how useful it was for to the Bulls' success, we should look at his point per possession rate in relation to his other high usage teammates. The three other main contributors in terms of possessions used were Carlos Boozer, Luol Deng, and Joakim Noah. Calculating their points per possession rates the same way we get:

Points Per Possession

Derrick Rose- 1.1

Carlos Boozer- 1

Luol Deng- 0.82

Joakim Noah- 0.33

So, Rose and Boozer were relatively positive players in terms of scoring while Deng was a slight negative and Noah was extremely poor. Now that we've looked at the Bulls' scoring, is there anything else that stands out that may have contributed to the team's success?

The thing that stands out to me is that the Bulls took 6 more shots and 7 more free throws. They were slightly less efficient than the Pacers (49% true shooting to the Pacers' 50.9%), but those extra shots made up more than the difference in efficiency. Why did those get those extra shots?

Well, the Bulls turned the ball over four more times than the Pacers. But, the Bulls grabbed eleven more offensive rebounds. Even though an offensive rebound doesn't technically begin a new possession, there is obvious value in extending the possession. We'll call that an "extra" possession where the Bulls got an opportunity to put up one more shot. They lost four opportunities through their turnovers relative to the Pacers, but their offensive rebounding edge gave the Bulls a chance to put up shots in seven more possessions (11-4) than the Pacers.

As discussed previously, the Bulls true shooting percentage for the game was 49%. That equates to 0.98 points per shot. Take those seven extra opportunities to take a shot, multiply it times the 0.98 points per shot rate, and you get 6.86 "extra" points for the Bulls. They won by 6.

So, what does all of this tell us? Derrick Rose's scoring was a definite positive for the Bulls. He scored at a higher point per possession rate than any other high usage players for the Bulls, and he did it while shouldering an enormous burden (45% usage). But, the Bulls' offensive rebounding probably played an even bigger factor in determining the outcome. Boozer had 5 offensive rebounds, Deng had 3, and Noah had 6. While Deng and Noah didn't have great scoring days, they contributed on the boards. Boozer contributed in both regards and, in doing so, was arguably the Bulls' most important player in Game 2.

Daily Dictionary: Usage (USG%)

noreply@blogger.com (Matt M.) — Wed, 20 Apr 2011 12:36:00 -0500

Usage (USG%)- 100 * ((Field Goal Attempts + 0.44 * Free Throw Attempts + Turnovers) * (Team Minutes Played/5)) / (Minutes Played * (Team Field Goal Attempts + 0.44 * Team Free Throw Attempts + Team Turnovers))

A player's usage is derived by calculating the percent of plays used by a specific player when he is on the floor. Usage, like other advanced NBA stats, is actually quite intuitive for anybody who watches a moderate amount of NBA basketball. The league average usage is 18.8%.

The Top 8 in Usage from the 2010-2011 regular season were:

1. Kobe Bryant - 35.1%
2. Derrick Rose - 32.2%
3. Carmelo Anthony - 32.0%
4t. Dwayne Wade - 31.6%
4t. Russell Westbrook - 31.5%
6. LeBron James - 31.5%
7. Amare Stoudemire - 30.9%
8. Kevin Durant - 30.6%

All of these players share a few key traits:

1. They are all viable #1 options for an offense
2. They are all adept at creating their own shot
3. They are all at or near the top of the league in Points Per Game

In our last daily dictionary, Matt W. explained the concept of efficiency by defining and applying True Shooting Percentage (TS%). Here I will show you how to use TS% and USG% together to analyze a player's production. The key takeaway is that when all variables are held constant, a player who sees his USG% rise will generally see his TS% fall. This is because a player who is tasked with being the team's primary source of offense will often see a greater number of lower quality looks, as coaches and teammates alike recognize that a bad shot from their best player may often be the best look they can get on any given offensive possession.

Combining usage and true shooting as elements in your analysis can help provide a more nuanced understanding of offensive contribution. Often a player will be overrated for his bulk scoring; a deeper look into the interplay between usage and efficiency will show that said player gets his points by virtue of dominating the ball.

Daily Dictionary: True Shooting Percentage

noreply@blogger.com (Matt Way) — Tue, 19 Apr 2011 14:30:00 -0500

True Shooting Percentage (TS%)- Basketball

True Shooting Percentage measures shooting efficiency taking into account the different values in 2 point field goals, 3 point field goals, and free throws.

The formula for calculating TS% is: Points/(2*(Field Goal Attempts + (.44*Free Throw Attempts)))

Ever look at a basketball box score and struggle to figure out a player's efficiency? It can be difficult. You have to look at 2 point field goals, 3 point field goals, and free throws all at the same time! True Shooting Percentage does it all for you. True Shooting Percentage recognizes that 3 point field goal is worth 50% more than a 2 point field goal, as well as that most free throws come in pairs.

NBA Playoff Preview Podcast

noreply@blogger.com (Matt Way) — Mon, 18 Apr 2011 21:17:00 -0500

Matt, Matt, and Julian discuss the NBA Playoffs with their predictions.

Note: This podcast was done before match-ups were set and some match-ups have changed. The analysis in the podcast should still, however, provide a nice preview of the playoffs and what to expect from particular teams.