I have been teaching for 5 years and every year I feel like my principles and district people get more and more into data. We all give the same assessments and EOCAs so they can look at the data and make decision. The worst part is most of these people have taken the single statistic class that is required to get their masters and don’t understand much about data. (funny side note: At a meeting one time, our principle was showing predictive data. The data said our students were predicted to do 4% better on our high-stakes test this year. My principal asked the following question to the staff, “Do you guys know why we want to get this percent higher? Because the margin of error is ALWAYS +/- 5% and we want to be out of the margin of error.”) Regardless, because our bosses value the data from these assessments, these affect the way teacher teach. This is no new secret, bad high-stakes tests and bad summative assessments can lead to bad teaching. What is interesting, is that this is not limited to teaching. The NBA for years used statistics that did a bad job at assessing the effectiveness of a player. Michael Lewis wrote an article in 2009 about the NBA player Shane Battier titled, The No-Stat All-Star. The gist of the article is that Battier doesn’t accumulate a lot of the standard statistics (points, rebounds, assists, ect.), but because of his unselfish play, he is as effective as some of the more well known NBA all-stars such as Carmelo Anthony or Vince Carter (This is 2009 Vince Carter). Here is a quote from the article, where Daryl Morey (The GM of the Houston Rockets, the team Battier played for in 2009) discusses how they do a better job at measuring the effectiveness of players:
“…the big challenge on any basketball court is to measure the right things. The five players on any basketball team are far more than the sum of their parts; the Rockets devote a lot of energy to untangling subtle interactions among the team’s elements. To get at this they need something that basketball hasn’t historically supplied: meaningful statistics. For most of its history basketball has measured not so much what is important as what is easy to measure — points, rebounds, assists, steals, blocked shots — and these measurements have warped perceptions of the game. (“Someone created the box score,” Morey says, “and he should be shot.”) How many points a player scores, for example, is no true indication of how much he has helped his team. Another example: if you want to know a player’s value as a rebounder, you need to know not whether he got a rebound but the likelihood of the team getting the rebound when a missed shot enters that player’s zone.”
Morey goes on to show other instances of how standard statistics create selfish players.
“Taking a bad shot when you don’t need to is only the most obvious example. A point guard might selfishly give up an open shot for an assist. You can see it happen every night, when he’s racing down court for an open layup, and instead of taking it, he passes it back to a trailing teammate. The teammate usually finishes with some sensational dunk, but the likelihood of scoring nevertheless declined. “The marginal assist is worth more money to the point guard than the marginal point,” Morey says. Blocked shots — they look great, but unless you secure the ball afterward, you haven’t helped your team all that much. Players love the spectacle of a ball being swatted into the fifth row, and it becomes a matter of personal indifference that the other team still gets the ball back.”
I feel this same problem occurs in education. When we value simple statistics, such as test scores, we create selfish teachers. We say things such as, “Don’t worry about that, its not on the test”, or “The best way to get the answer is to just plug in the 4 multiple choice answers.” Maybe the more detrimental part of selfish teaching is it limits the types of tasks we do in our class. For example, a teacher might say, “I am not going to do that Mathalicious lesson because it doesn’t exactly go over the problems that are on our district created tests.” or, “I am not going to do Estimation 180 because my students don’t need to estimate on the AIMS test. I am going to work on adding/subtractice integers instead.” When we value simple statistics like test scores, we reward teachers for selfish teaching. The NBA has become better at finding effective, less-selfish players by developing advanced metrics to measure the effectiveness of players like Shane Battier. Here are some examples of new NBA advanced statistics:
Opponent Field Goal Percentage at the Rim (Opp FGP at Rim): This stat measures an opponent field goal percentage at the rim when you are defending them. If there is a low percentage, that means you defend well at the rim. If there is a high percentage, it means you do not defend well at the rim. The lowest percentages for Opp FGP at Rim is Bismack Biyombo at 38%. Biyombo rates 21st in blocks which is usually the statistic we use to measure great rim protectors. On the other hand, DeAndre Jordan who is second in blocks, rates 36th in Opp FGP at Rim. To the casual fan, we think of Jordan as a better rim protector because of his high number of blocks, but really, it is Biyomo.