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06: Move on

Biggest takeaway this week was remember to move on. Don’t stay mad. Drive forward. Shit happens. Keep on keeping on.

Consuming

More Scorecast

Finished reading Scorecasting by Tobias J. Moskowitz and L. Jon Wertheim this week.

Continuation of notes below:

Chapter 7: Rounding first: people have a bias to round numbers. It’s why more batters hit .300 than .299.

A batter is close to .300 at end of season? He won’t play.

This is incentive bias. A rounder number (100 RBI, .300) usually leads to better salary.

Bobby Abreu finished with exactly 20 or 30 home runs five times, seven times exactly 100 or 100 RBI, and no seasons with 95 to 99 RBI.

Same true in NFL with 1,000 rushers.

You can find value in looking at players just below these benchmarks.

People view acts of omission - the absence of an act - as far less harmful than acts of commission - actually committing the act, even if the outcomes are the same or worse.

Chapter 8: Thanks, Mr. Rooney: the drop-off of black coaches winning is actually a good thing.

Why?

It validates the Rooney rule. Black coaches are held to same standard as white counterparts. They’re equal.

Chapter 9: Comforts of Home: there is a home-field advantage in all sports. It’s a bit more strong in some sports, but it’s the same regardless of where sport is played.

There is significant incentive to win at home - concessions, people leave happy, spend more money.

But why does home field advantage exist?

Because of crowd support? No. Look at kicks in football, free throws, shootouts - when you control for a situation where crowd could impact game, it really doesn’t.

Because of travel? No, evidence isn’t strong that travel plays a major role.

Because of scheduling? Yes.

NBA teams play lots of back-to-back games, and this does favor the home team. It’s less of an issue in certain sports like MLB.

More extreme in college football. Teams pad schedule with hosting weak opponents.

Scheduling bias explains some of home field advantage, but not in baseball or NFL or soccer at all.

Because of unique home characteristics? Not really, some impact, but not huge difference. Hard to really have an edge with unique home fields today.

Chapter 10: What is driving home field advantage officials or referee bias is most significant factor.

Study used QuesTec data to determine officials or umpires side with the home team in pressure situations on borderline calls.

More ambiguous the call? More likely to go in home team’s favor.

Why? Confirmation bias.

Humans conform because they want to fit in and because they believe the group is better informed than they are.

Officials aren’t really aware of the bias, it’s just a natural human response.

Answered three questions:

1) Why does home field advantage differ across sports?

Because refs or officials are more important or have more influence on the game. Soccer more than baseball. A penalty kick is huge, where umpire calls are fairly black and white.

2) Why is it the same for a sport no matter where its played?

Because the role of the referee is the same no matter if soccer is played in America or Europe or Spain.

3) Why hasn’t it changed much over time?

Because the officials role hasn’t changed with the rules.

Chapter 11: No I in Team: But there is an m and an e.

There is a bigger influence of superstars in certain sports like basketball.

Disagree with soccer, think it’s much less.

Makes sense in basketball. Only 5 players play at a time.

Where in baseball, a player might only get 3 at-bats a game. In soccer, only a few touches by your best attacker.

Michael Jordan - There is an I in win. So which way do you want it?

Chapter 12: Off the chart Mike McCoy came to own the NFL draft using a value chart that was largely designed off assumptions.

Tried to answer - what is the value of a draft pick in a certain round?

Value of the first pick in draft was 3000, and first pick in second round 580, and final pick was 0.4.

The chart just gave the Cowboys a framework.

It more or less stated value in multiple picks versus one high pick.

Opposite is when the Saints traded eight draft picks for Ricky Williams. Overpaid by a ton.

Eventually when all teams started using the chart, it became obsolete. Thaler and Massey found inefficiencies.

Teams would overpay to pick today versus playing the long game. Not comfortable with uncertainty.

It takes talent to evaluate talent.

Draft pick value is different for different sports. Seven rounds in NFL, just two in NBA, and 50 in MLB.

Find value by looking at the details. Ask why.

Brady didn’t play at Michigan more because of political reasons than his ability.

Chapter 13: Coin toss trumps all: If you won the coin toss in overtime for NFL, you win 61 percent of time.

This prompted changes in the NFL overtime rule. It wasn’t really fair.

Chapter 14: What isn’t in the Mitchell Report Dominican players were more likely to take steroids because of cultural and situational needs. All incentive bias.

The upside was far greater for Dominican players than American players. A lot of Dominicans were much younger, where American players were older and trying to get one last big contract.

MLB gave players choice to act immorally and from an economic perspective, act irrationally.

Chapter 15: Does icing the kicker or shooter work? No. It’s a lukewarm strategy.

Study looked at NFL kicks and NBA free throws. Found no difference in performance.

Chapter 16: Myth of the Hot Hand: Momentum in sports doesn’t exist. We just believe it does because we like patterns. We don’t like mystery.

Randomness and luck resist explanation.

Try flipping a coin 100 times. There will be streaks of heads or tails. Those streaks don’t increase the odds the next flip will land on heads.

We do a really poor job at understanding randomness. You aren’t due for anything. Your chances do not change because of a streak.

The true quality of teams can be measured in large sample sizes. Small samples are dominated by randomness.

Chapter 17: Damned statistics You can make numbers say whatever you want. It’s selective. And more than one thing can be true.

Be objective. Neutral. Look at as much data as possible to get a more accurate picture.

Not just short-term numbers.

Chapter 18: Are the Cubs cursed? No, they’re not cursed or super unlucky.

It was deeply ingrained in the team that wins and losses didn’t matter. Wrigley wanted fans to enjoy the ballgame, win or lose. Lovable losers.

Cubs could lose and attendance could go up. This wasn’t the case for almost all other teams.

Beer prices remained flat. Cubs fans went to game for experience, not for wins.

Curses aren’t to blame or Steve Bartman.

Interesting to see how Cubs expectations have now changed since winning a World Series.

Epilogue: it was really hard to name the book.

What does title mean?

Ignore data and diverse views as your own peril. Seek controversial or opposite opinions and challenge convention to improve your decision making.

Scorecasting.com no longer exists.

Quotes I liked this week

Creating

R snippet for charts this week:

trstl.data <- read.csv(file="trstl.csv")
View(trstl.data)

p <- ggplot(trstl.data, aes(x = Year, y= TORank, colour = variable)) + 
  geom_line(aes(y = TORank, colour = "TORank"), size=1.5) +
  geom_line(aes(y = STLRank, colour = "STLRank"), linetype="dashed", size=1.5) +
  labs(x="Year", y="Defensive Turnover Rate and Steal Pct Ranking (out of ~351)",  
  title="Carolina: Division-I Defensive Turnover Rate and Steal Percentage Ranking Since 2004", 
  subtitle="Percentage of an opponents possessions that result in a turnover or a steal.", caption="Brought to you by dadgumboxscores.com, data via Kenpom.com")  +
  scale_x_continuous(name="Year", breaks=c(2004:2018), labels=c(2004:2018),limits=c(2004,2018)) +
  scale_colour_manual("", breaks = c("TORank", "STLRank"), values = c("TORank"="#829356", "STLRank"="#9A2617")) +
  theme_ipsum(grid="XY") 

Takeaways

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