After hearing everyone slurp Brett Favre’s skills and bash Jake Delhomme’s for the past year, I decided I wanted to take a quick look at whether the praise and venom were warranted.
Personally, I have always viewed these two quarterbacks as similar players: Southern gun-slingers who can win or lose a game for you with their arms. Delhomme certainly doesn’t have the pedigree and the mythology that Favre has. But if you asked me to put money down, I would say that Delhomme’s stats would bear him out as, essentially, a poor man’s Brett Favre.
Tonight, I tested that theory. Here’s what I found in some simple passing metrics:
*Note: to try to give the most accurate view, I’ve removed the following outlier seasons from the calculations:
Favre’s first season in Atlanta (where he threw only 4 passes)
Delhomme’s two seasons as a back-up in New Orleans (where he threw only 76 and 10 passes, respectively)
Delhomme’s 2007 campaign with Carolina (where he played in only 3 games and threw a mere 86 passes).
CAREER PASSING STATISTICS (Favre | Delhomme)
Completion %: 62.0 | 58.9
Yards / Pass Attempt: 7.1 | 7.2
Avg TD / season: 27.6 | 18.7
Avg INT / season: 17.5 | 14.7
TD to INT ratio: 1.58 | 1.27
INT%: 3.22 | 3.53
TD%: 5.11 | 4.18
Career Win %: 63.5 | 58.7%
So what do we learn from this comparison? I would argue that it proves my basic premise (which I think is a first for when I do a real analysis of one of my hunches). Favre makes a bigger impact on scoring and winning percentage than Delhomme. He throws significantly more picks per season than Delhomme, but only from a raw numbers standpoint. Based on percentage of passes intercepted, Favre is actually +0.3% more accurate than Delhomme. But that’s a marginal difference.
However, I have one more trick up my sleeve.
I noted in the above the stat comparison that I was removing outlier seasons, which is to say, statistical anomalies. In the first table, I did that strictly by taking out any seasons where the two QBs threw fewer than 90 times.
But as we all know, Brett Favre had easily his best statistical season in 2009 at age 39. This is, in a sense, the mother of all outliers. So if we remove Favre’s epic first season with the Vikings from the equation as well, the comparison changes to this:
CAREER PASSING STATISTICS (Favre | Delhomme)
Completion %: 61.8 | 58.9
Yards / Pass Attempt: 7.0 | 7.2
Avg TD / season*: 27.3 | 18.7
Avg INT / season*: 18.1 | 14.7
TD to INT ratio*: 1.51 | 1.27
INT%: 3.32 | 3.53
TD%: 5.04 | 4.18
Career Win %: 62.5 | 58.7%
Perhaps the greatest lesson to learn from the revised table is this: when a guy plays for 19 years, removing one of those years—even if it’s his best—doesn’t make all that big a difference. But it’s also worth noting that 2009 was only Favre’s 6th best in terms of TD% and 5th best in terms of raw number of TDs. It was, however, his career best in terms of completion %, yards per attempt, raw interceptions, and INT%.
But the big take-away for Browns fans remains this: Jake Delhomme doesn’t provide as much firepower as Brett Favre, on either a raw or a percentage basis. However, his decision-making and accuracy are comparable. All signs point to Delhomme throwing fewer overall INTs than Favre and being roughly equivalent in INT%, only slightly worse in Completion %, and ever so slightly better in yards per pass attempt in 2010.
In short, if you look at any other category besides the number of points on the board, it’s reasonable to say that Delhomme nearly duels Favre to a draw.
But the most important statistic for the watchability of the 2010 Browns is Delhomme’s career win percentage as a QB. 58.7% would equate to roughly a 9-7 record. I don’t expect the Browns to hit that mark in 2010, but I am encouraged that barring another catastrophic outlier of a season, Delhomme is in position to improve the Browns’ offense dramatically this year. He may not be Brett Favre, but then again, the Browns also come way out ahead of the Vikings in this statistical category:
Jake Delhomme 2010 Salary: $7MM
Brett Favre 2010 salary: $17MM
So from a financial efficiency standpoint, the Browns may actually be doing fairly well for themselves at the QB position. Starting Sunday, though, we’ll find out what seven stacks really gets you in the 2010 NFL.
After a discussion with WP48 expert Holland, I realized that I semi-botched my projection of the Cavs’ team performance this upcoming season. I wanted to take some time tonight to correct the analysis.
For those of you actually interested in advanced statistics, the main error involves some of the nuances of WP48. The purest form of the metric involves position adjustments. For example, a point guard’s WP48 performance isn’t weighted in exactly the same way as a center’s. Without adjusting, the metric heavily favors big men because they’re so much more likely to, say, get rebounds and shoot a higher percentage, as well as much less likely to turn the ball over (all important components of the entire scheme).
My source for WP48 was Basketball Reference, which is still a fantastic resource for advanced stats. However, if anyone out there wants to use it, keep in mind that the WP48 figures they give appear to be unadjusted.
OK, with the explanation out of the way, here are the adjusted WP48 totals for the roster hold-overs from last season. I kept all of the other parameters the same as in the original post.
Varejao = .181 WP48 x 36mpg x 82 games = 11.1 wins
Mo = .116 WP48 x 36mpg x 82 games = 7.1 wins
Moon = .191 WP48 x 36mpg x 82 games = 11.7 wins
Hickson = .123 WP48 x 20 mpg x 82 games = 4.2 wins
Gibson = .042 WP48 x 9 mpg x 82 games = 0.6 wins
Jamison = .194 WP48 x 36 mpg x 82 games = 11.9 wins
Parker = .081 WP48 x 36 mpg x 82 games = 5.0 wins
Powe = .000 WP48 x 9 mpg x 82 games = 0 wins
Green = .074 WP48 x 9 mpg x 82 games = 1.1 wins
Jawad = -.069 WP48 x 9 mpg x 82 games = -1.1 wins
Telfair = .020 WP48 x 4 mpg x 82 games = 0.1 wins
TOTAL PROJECTED WINS PRODUCED BY 2010-11 CAVS: 52 wins
So after adjusting for position, WP48 actually projects the Cavs to win one additional game beyond what the unadjusted numbers projected. This is mostly due to dramatically increased ratings for Moon and Jamison. Their gains more than offset significantly lower ratings for players like Mo, Gibson, Parker, Powe, and Jawad.
The other error I made in Monday’s post had to do with the expected error involved in the calculation.
I said at the time that WP48 predicted win totals to about 80% accuracy. I undershot. It’s actually (allegedly) accurate to within 94%. The error range is about +/- 1.5 games. Essentially, what this means is that the 2010-11 Cavs have a 94% chance of winning somewhere between 50.5 and 53.5 games.
Let me repeat that: if no changes are made to the current roster, and the rotation looks something like what I’ve outlined above, the Cavs allegedly have a 94% chance to win at least 50 games in their first season without LeBron. That’s +20 wins higher than what even the most optimistic sports pundit (that I’ve seen, anyway) has predicted for the team next season.
Of course, if Byron Scott chooses to start Jawad at 3 over Moon, this entire projection goes out the window. And I might, too. (Though I live on the first story, so the effect would probably be pretty muted.) But for now, things are definitely looking up.
Now, as I noted in my original post on this topic, I still think we’re running a significant risk in these projections if we only look at the numbers from last season. (Unfortunately, I don’t have adjusted career WP48 numbers.)
One of the points where I split with the WP48 philosophy has to do with synergy, for lack of a better term. The WP48 system assumes that player production is an inherent trait determined by the player’s skill level. In other words, he will produce roughly the same over time regardless of who the other 4 men on the court next to him are, not to mention who the 5 defending him are.
I, on the other hand, believe that a player’s teammates have a significant effect on what he’s capable of doing. For example, a spot-up 3-point shooter gets much better looks if he’s playing with someone on the front line who demands a double-team. Theoretically, he should be more open for his shots and should drain a higher percentage. I would argue that Mo Williams has been a great example of this. In his 2 seasons with the Cavs, he’s shot 43.6% 3P and 42.9% 3P, respectively. In the previous 4 seasons in which he’d played significant minutes with Milwaukee, his career high from beyond the arc was 38.5%. That’s a 4.5-5% uptick since starting next to LeBron.
This will be one of the elements to keep your eye on this coming season. Can the Cavs players find a way to do what they did with LeBron? As Mike pointed out, their current roster has a very low number of guys capable of creating shots for themselves. We’ll see how that affects things, along with the new up-tempo system Scott is already implementing in the Vegas Summer League. (Side note: WP48 disregards pace, so in theory this shouldn’t affect the projected win total. I remain skeptical on this point, but that’s a discussion for another day.)
That said, if things proceed as I expect, I still plan on checking into the over/under on Cleveland’s win total for 2010-11 if I’m in Vegas before the season starts.
All right, I think this is the last numbers-centric post I’m doing for a little while. Pseudo-philosophy, pseudo-psychology, and pseudo-humor back next week.
With eight games to go in the regular season, the Cavs are on the verge of locking up the #1 seed in the league for the second straight season. This, of course, means that playoff predictions are beginning to start up in earnest.
I’m not going to start looking at first round opponents just yet because the playoff seeding is still liable to shift too much her in the final two weeks. (I’d prefer not to waste a bunch of time talking about a first round match-up that never happens.) Instead, I want to begin to mentally prep myself (and everyone else, if they care) for the likely Eastern Conference Finals that we’ve been waiting for since last June: Cavs V. Magic, part 2.
Obviously, there are no guarantees that this series is going to happen, either. But practically every statistical measure I can find suggests that it should. Not only that, but the numbers also that if/when the match-up itself does happen, it’s likely to be excruciatingly close.
Here’s what the advanced team stats say right now.
Offensive Efficiency (points scored per 100 possessions):
Cavs = 109.2 (2nd); Magic = 108.5 (5th)
Defensive Efficiency (points allowed per 100 possessions):
Cleveland and Orlando are the only two teams in the East that rank in the top 10 in both Off Eff and Def Eff. Atlanta is the only other East team to rank in the top 10 in Efficiency Differential, but their 8th-place number (+4.62) pales in comparison to the Cavs and Magic. It’s better than half, but not by much.
In fact, even the third place team in Efficiency Differential—the mighty Los Angeles Lakers—takes a noticeable step down from the top two. After tonight’s loss to the Hornets, LAL’s differential drops to +5.97, or less than two-thirds of the Cavs’ and Magic’s.
In a nut shell, what does all this mean? Basically, that if you give every team in the league an equal number of possessions, the Cavs and Magic are going to be the best overall at both scoring and defending. They’re also going to be noticeably in a different class than the third place team (LAL) from an overall standpoint.
In addition, John Hollinger’s power rankings rate Orlando and Cleveland as #1 and #2, respectively, in the league as of tonight. While Hollinger doesn’t explain the exact formula for his rankings, he does explain that it weighs a number of factors that the efficiency metrics I mentioned above do not. For instance, Hollinger’s system tries to control for strength of schedule. It also lends more statistical value to a team’s recent performance than to their early performance. While the exact metrics vary depending on the point in the season that the rankings are done, at this point, the system values the most recent 25% of games more heavily than the previous 75%.
If we look at Hollinger’s actual ratings (i.e. the numbers themselves), we see a repeat of the general conclusions of the efficiency differential rankings. Orlando currently ranks first with a score of 107.683; Cleveland ranks second with a score of 107.325; and Atlanta is the only other East team to rank in the top 10. (Their 104.003 ranking sits them in the #9 spot.)
In short, Hollinger’s system suggests that not only have the Cavs and Magic been the two best teams in the league over the entire season, they’ve also been the two best teams over the past few weeks. If it’s not immediately clear why this matters, consider the Celtics. They began the season looking like the true title contenders most of the old school experts predicted them to be. Right now, though, the Cs rank 11th in Hollinger’s system, due largely to their post All-Star break performance, which can hardly be labeled much better than “decent”—especially if you value things like scoring margin over wins and losses (which Hollinger’s system does).
Simply put, the Celtics are still winning a fair amount of games, but the wins aren’t by as much and the losses are by much more than they were early in the season. And if Hollinger is to be believed, this matters more than their final record. Exhibit A in this category is the 2007 Dallas Mavericks and San Antonio Spurs. The Mavs finished the season with 9 more wins than the Spurs, but the Spurs killed the Mavs in scoring margin. The 67-win Mavs were then bounced in the opening round by Golden State, while the Spurs went on to win the title.
Conversely, the Cavs and Magic are both still balling at a high level.
Oh, and just to round out the list of team stats, the Cavs are 2nd in the league in True Shooting Percentage at 57.3%; the Magic are 3rd at 56.8%; and the Cavs are also 2nd in the league in Total Rebounding Rate (% of available rebounds grabbed) at 52.45, while the Magic are 4th at 51.87. In case you’re wondering, the teams separating Cleveland and Orlando from the top of the list AND from each other in both of these last two categories are all Western teams.
In fact, what becomes really remarkable as you go down the lists is how close the Cavs and Magic are even in some of the categories where neither performs particularly well. For instance, Orlando is tied for 19th in the league in Turnover Rate (% of possessions ending in a TO) with a 13.69 rating; Cleveland is 17th with a 13.58 rating. Orlando is 25th in Offensive Rebounding Rate at 24.49; Cleveland is 21st at 25.18. So neither team is particularly great on the defensive glass, and neither is particularly good at holding onto the ball. Yet in both of these cases where they suck (relatively speaking), the Cavs and Magic still suck about equally as bad as one another.
Simply put, both teams meet strength with strength and weakness with weakness. They also even play the game at an almost identical pace. Orlando averages 93.7 possessions per 48 minutes (24th overall); Cleveland averages 93.0 (26th). As Mike pointed out recently, the pace similarity also suggests that the two teams are governed by very similar coaching philosophies: execute in the half-court unless a blatant opportunity to run presents itself, because this approach will best allow you to get back on defense effectively.
Again, there’s no way for us to know how any of this is going to play out. Injuries or statistical anomalies could prevent the match-up from either happening at all, or from playing out in an unexpected way. But one thing is for certain: if the statistics are accurate, there is no feasible way that I’m going to be able to watch the Eastern Conference Finals sober, because the victor is going to be determined by the thinnest of margins.
Frequent JMID reader Holland sent Mike and me an email earlier today about a particular aspect of Mike Brown’s rotation that has been driving him insane. We decided to put it up as a Guest Post to spark debate among readers.
For those who may not have picked up on it, Holland is a staunch advocate of the Wages of Wins Journal and their Wins Per 48 (WP48) system of performance evaluation. As a direct result, he is also quite possibly the world’s biggest fan of Troy Murphy, AKA sweet-shooting Dino Velvet.
Holland’s analysis in its entirety is posted below. Instead of WP48, he’s used season-long per-40 minute stats as the basis for comparison. He has also refused to name the players in question, but it’s probably not terribly difficult to figure out who they are (both are reserves).
I’ve attempted to get the discussion going by posting a few of my own reactions to Holland’s thoughts in the Comments section. We invite everyone to read, consider, and react.
Without further ado…Holland:
Suppose you’re an NBA coach. You have two players who play the same position. How do you decide who gets more minutes?
You could just watch them play and make a totally subjective decision, but that doesn’t seem very smart in an industry where many millions of dollars are at stake. A more objective approach should - at the very least - have a strong impact on your decision. I’m talking about statistics.
So here is a very, very simple box score comparison - just their per 40 minute stats side by side (to compare apples to apples):
FG%
Player A: 43.9 Player B: 41.4
Advantage A —- 3 Pt %
Player A: 29.3 Player B: 33.8
Advantage B —- FT %
Player A: 86.1 Player B: 74.1
Advantage A —- Rebounding
Player A: 7.5 Player B: 4.4
Big advantage A —- Assists
Player A: 1.9 Player B: 2.2
Advantage B —- Turnovers
Player A: .9 Player B: 1.0
Tiny advantage A —- Steals
Player A: 1.2 Player B: .7
Advantage A —- Blocks
Player A: 1.2 Player B: .4
Big advantage A —- Fouls
Player A: 2.7 Player B: 4.0
Advantage A —-
Final Score:
Player A: 7 (2 big advantages) Player B: 2 —-
This is assuming all stats are created equal, which they are not. For instance, rebounding is more important that getting assists, so Player A’s big rebounding advantage is far, far more significant than Player B’s slightly better assist average.
Seems like a pretty cut and dry case to me - you’ve got to go with Player A, and it’s not even close.
Technically, the Cavs lost tonight on a buzzer-beating 3 by D-League call-up Sundiata Gaines. However, the game was really lost the whole way through due to the Cavs’ well-established Achilles heels: turnovers, poor free-throw shooting, and a tendency toward stagnant offense.
The team coughed the ball up 21 times tonight, or one turnover every 2 minutes, 18 seconds. They missed 10 free throws (25 / 35 for 71.4%), including two in the deciding minute of play. They assisted on just over half of their makes from the field (51.7%) for a raw team assist total of 16, versus Utah assisting on 78.3% of their made shots for 25 raw team assists.
To my eye, the Cavs’ defensive rotations were a step slow for the entire first half and portions of the second, but I can’t criticize their effort too heavily when they held the Jazz to 40.5% shooting from the field and won the rebounding battle, albeit narrowly (+1 in the raw rebound total, +1.4% in Total Rebounding Rate).
On the season, Coldstone’s Crew is 11th in the league in Turnover Rate, or the percentage of a team’s total possessions that end in a TO. (The Cavs’ TOR is currently 14.06.) Doesn’t sound bad until you realize that this is one of those scales where you want to be 30th. Of the accepted contenders, only Boston is worse — 2nd overall, in fact, with a TOR of 14.93%.
Meanwhile, the Cavs are 21st in the league from the charity stripe (73.9% as a team). Of course, they were below that average tonight. As we’ve preached before, most times the differences between good and bad teams in these statistical ratings really boil down to just a handful fewer makes / misses. Tonight was one of those games where 1-2 more made free throws would’ve been the difference in the game.
On the up side, the Cavs are actually 7th in the league in both Assist % — with dimes on 57.6% of their hoops, — and Assist Rate, which is the number of total possessions ending in an assist. (To clarify the difference between AST% and AR: AST% tells us only what percentage of made baskets were assisted, whereas AR tells us the percentage of all possessions that ended in an assist.) So I should shut up about that. They had an off-night, but they’re one of the better teams in the league when it comes to moving the ball, despite what the mainstream media would have you believe.
The problem is that Utah leads the league in both of these categories with a 67.08AST% and 24.42 AR. Didn’t help that the Jazz eclipsed that average AST% by a country mile tonight (78.3AST% for the game).
Considering that we’re officially halfway through the season, it’s becoming increasingly likely that these areas are going to remain persistent vulnerabilities for this particular Cavs team. They are definitely two areas of concern when it comes to playoff time and possessions are at a premium. I’m not saying anyone should panic, especially considering the continued strength of the defense. But as the Cavs proved again tonight, some times these issues can kill you.
Holland and I will be at Staples Center for the finale of the road trip against the Clippers this Saturday. I’m considering trying to do a retroactive live blog like my previous Indians one back in the summer. We’ll see if enough noteworthy things happen at the arena. It’s really all about the atmosphere.
