in Hoops

Is Le’Bryan Nash a bad big-game player?

Photo Attribution: Emily Nielsen

Photo Attribution: Emily Nielsen

Kyle asked me to take a look at how Nash & Smart played in big games compared to games against weaker opponents. I decided to throw in Markel as well since he’s playing at an all-conference level this year.

I took a different approach this time around. I used Dean Oliver’s offensive rating, as calculated by Ken Pomeroy. Here’s how Pomeroy describes it on his site:

Offensive rating (ORtg): A measure of personal offensive efficiency developed by Dean Oliver. The formula is very complicated, but accurate. For a detailed explanation, buy Basketball on Paper.

Let’s just take his word for it. The only thing I don’t like about this stat is that it’s kind of hard to quickly determine what a good figure is without comparing it to other players (Smart has an offensive rating of 103.3. Out of context, it doesn’t mean a thing).

I didn’t want to simply find games where players were efficient, though…I wanted to find games where the players really showed up and played well. Here was my approach: I multiplied each players offensive rating times the % of possessions the player used in each game. The higher the result, the more positive an impact the player had in that game.

Here’s an example. Let’s use the single-best game in this category by an OSU player this year: Markel’s dominating performance at Texas Tech (25 points on 11 shots, 2 rebounds, 4 assists). Markel had an offensive rating of 192 (for context, the leading player in the country has a season average of 128). He was involved in 31% of the team’s possessions that day. So his stat for the purposes of this post is 192 * 31% = 59.5. No one has topped this mark so far this year.

On the other hand, let’s look at the worst game by this group of three: Le’Bryan’s no-show against West Virginia at GIA. He played 25 minutes but he was 0-4 from the field and scored only 2 points. His offensive rating was 66 and he used 9% of the team’s possessions, so his stat for this post is 66 * 9% = 5.9.

Now, the real question: who has the most good games against good opponents. To the scatter-plot machine!

In all of these charts, the best games are at the top and the worst games are at the bottom. Opponent strength is highest at the left side and lowest at the right.

Marcus Smart had his best game of the year against our toughest opponent, Gonzaga (23 points on 15 shots, 4 rebounds, 6 assists). It’s really not that spectacular of a stat line for Smart, but he was involved in 40% of the team’s possessions that day. His worst game came in a 34-point win over Texas Tech at home.

His trendline is higher against our better opponents, meaning that he has played his best games in OSU’s biggest games this year.

Nash, on the other hand, has generally played better against weaker opponents. His monster game at TCU stands out quite a bit here…28 points on 16 shots and 6 rebounds. However, his recent improvement has given him a solid group of games against solid opponents (bedlam at home, the loss at Baylor, Iowa State). I don’t think him not showing up in big games is a valid criticism at this point.

Markel’s best game was at Texas Tech. His worst game was his stinker at Baylor (2 points on 10 shots – ouch!). Markel has a slightly higher average than Nash and a slightly lower standard deviation, meaning that he has played on average better and more consistently than Nash.

Here’s all three of these guys together. Their best game was the blowout win at Texas Tech, trailed closely by the very next game at home against OU. Their worst game was a 20-point win over Missouri State (whatever) followed closely by the home loss to Kansas. The low figures in that loss were mostly caused by low shooting percentage (the three guys combined for 12 for 39 shooting in that game — how were we in it again?).

But the three guys together have been remarkably consistent this year. Aside from the two outstanding games, most of them have been close to the trendline in the middle. It leans slightly higher against our weaker opponents, but there are plenty of good games in our biggest contests.

They’re all big from here on out…here’s to more downward sloping trendlines in our future.

  • David

    I really, really liked this post. Some interesting figures, and good explanations. And it makes sense that most players would tend to play a bit better against weak opponents and a bit worse against strong opponents (Smart is clearly not “most players”).

    It would be interesting to see what the slope of the average trendline for the whole team, conference, and nation looks like… I’m betting it would be at LEAST as sloped upwards as Brown and Nash, if not more. Also, it would be interesting to do a home vs away comparison.

    Well done.

    • http://pistolsfiringblog.com Kyle Porter

      Had it under the wrong author earlier but OKC Dave is who you need to credit. Amended now.

  • http://www.cowboysrideforfree.com mfc_crff

    My brain is now officially mush. [signs up for Stataholics Anonymous]

  • Will

    Wouldn’t % of possessions already be included in a state like ORtg? Is it calculated on a per game basis?

    • http://gravatar.com/okcdave okcdave

      ORtg is calculated on a per-game basis, and % of possessions is not included. Case in point: in Saturday’s game against Texas, Jurick was 1-1 for 2 points and grabbed 5 rebounds in 24 minutes. His ORtg was 201. His adjusted ORtg according to my calculation is 201 * 3% of possessions = 6.03.

  • http://twitter.com/tayloryork Taylor York (@tayloryork)

    #2 Rule in chart making – Axis labels!

    #1 would be title – you got that one. =D

    • http://gravatar.com/okcdave okcdave

      I know, I know. I was trying to get it wrapped up quickly. I summarized the axes before the Smart shart, but the labels would have made it a better post. You should start posting in red font…it would make it more like school.

  • http://sadastronaut.wordpress.com Chance

    I would expect a positive (higher toward right corner) slope for almost every player. Even a flat line would be impressive, but sloping the other way is amazing.

    • David

      +1

  • Nate

    Soooo… This just proves that Kyle was in fact cherry picking bad games from Nash.

    • Bro, I’m a Bro, bro. Slam some brews.

      Totally, bro.

  • Adam

    Good stuff Dave!

    I suspect a positive slope is not quite as unlikely as some are suggesting. It makes sense for the overall offensive rating to drop as the teams get more difficult, but remember we are plotting the ORtg*usage. I suspect what tilts Smart to a negative slope is that his usage rate increases against the better teams. He is content to get others involved against weaker opponents and take a back seat, but in big games he shoulders more of the load. I suspect this would be the case for many top tier point guards.

    While the question being asked necessitated evaluating the overall quality of the opponent, I think it would also be interesting to look at this versus the quality of the defense (rather than the quality of the opponent).

    Also, since you’ve already done much of the work, it would also be interesting to plot the top 10 individual games by OSU players this year.

  • dooley

    If Nash can keep up his trend of playing well lately, I like the way this team is headed.

  • dooley

    I’m ready for Wednesday. That would be a really big win at Hilton if we can pull it off.

  • Wade

    I like when you explain these space charts like I’m 5. Keep up the good work.