Game Review: Heat at Bulls- Game 1

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

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%

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

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

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

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

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

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

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

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.