Lakers Lent: Chuck should have fasted sooner and Historical Win Trajectories

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For the 2015 NBA season, the only exciting Lakers news is the return of the Kobe show and Charles Barkley’s Lakers Lent.chuck_madonna_churchThe Lakers started the season with 0 wins and 5 losses, amazingly bad. The round mound of rebound started Lakers Lent, fasting until the Lakers won. This week, Chuck finally ate and the Lakers finally got a win, advancing to 1 and 5 against the new look Charlotte Hornets. The following game, the Lakers lost to the Grizzlies.

What did Charles eat, is the question? Easy, I say organic foie gras milk shakes. The interesting question is, what other times in history have teams started 1 and 5? Starting under those conditions, where did they end up and what win trajectory paths did they follow?win_traj_w1l5For all historical 82 game seasons (thus excluding pre-1968 and the two lockout seasons) there have been 121 times where teams started 1 and 5, highlighted in cyan. Following these win paths, things look pretty grim. In general, teams end up in the tail of the pack, scum eaters, cocaroaches.

However, we notice a difference between seasons. In the current era, the final location at game 82 is more spread out (more variation), bottom feeder teams have more hope for positive win mobility, whereas teams in the older eras were stagnant (less variation), more likely remained near the bottom.

So, Chuck should have had many lents. Mavs Lent, Rockets Lent, and Knicks Lent. Before Chuck and Angelinos say “those aren’t the Lakers, they’re just wannabes that look like them,” theres some hope. That is, if the Lakers do not purposely go all out tank mode like the doormat 76ers.chuck_wannabe_fenceUp next, an interactive version that lets you choose the initial conditions.

PS

As my favorite statistician, NAS, said, “no ideas original under the sun.” Substituting professions for ideas, Hadley Whickham is a modern day blacksmith who is forging open access [R] weapons. All of this analysis is possible by open access statistics. Specifically, using a combination of rvest for web scraping data from basketball-reference, dplyr for shaping the data, and ggplot2 for graphics.

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