Patti Engineering Insight

Here is my recap of my simulated semi finals of the NCAA Playoffs which took place New Year’s Weekend:

Saturday January 1, 2011 (both games played in Pasadena, CA):

• Stanford (4) 38 – Arkansas (8) – 21: In a battle between the two quarterbacks who will likely be the first two taken in the NFL draft, Andrew Luck proved better. Stanford held a slim 17-14 lead at half time, but pulled away in the second half. Two second half interceptions by Ryan Mallett hurt Arkansas.
• Oregon (2) 30 – TCU (3) 21: TCU hung gamely holding a 14-13 half time lead, but the superior talent of Oregon won out in the end.  Sr. wide out Jeff Maehl had a big game with 9 catches and two long TD grabs. Sr. Quarterback Andy Dalton of TCU played well, but the Horn Frogs could never get a ground game going and were held to 30 yards rushing for the entire game. 

Summary:

• It will be an all Pac-10 final as the overall records by conference for the tournament will end up (ordered by Sagarin’s Conference Strength):
  • Pac-10 (2 schools): 7-1
  • SEC (3 Schools): 3-3
  • Big 12 (1 School): 0-1
  • Big-10 (3 Schools): 1-3
  • ACC (1 School): 1-1
  • Big East (1 School): 0-1
  • Mountain West (1 School): 2-1
  • WAC (1 School): 1-1
  • Conf USA (1 School): 0-1
  • MAC (1 School): 0-1
  • Sun Belt (1 School): 0-1
• The Finals will be held Monday Night January 10^th at the former site of the Sugar Bowl (New Orleans, LA). This is the EXACT same date that the 2010 BCS championship game is being played.
• By hosting the opening round at the higher seeded team’s home site, and then having four schools playing at the former sites of the Orange, Rose, and Fiesta Bowls, the host committees actually saw attendance go up over what a two school event would have had. More Hotel Rooms were booked, and more money was spent at local establishments. The sites will rotate every year, so that next year Pasadena will host the national championship game and New Orleans will get two Quarter Final Games.
• There would still 20 bowl games that hosted teams 17-56 to fill the Christmas vacation with meaningless College football games.
• I just read that Andrew Luck is staying at Stanford. I wish him luck with that decision. An injury or a bad season next year will cost him millions of dollars.

Data is great. The ability to collect and store oceans of data has been the greatest achievement of my lifetime. We now have more data on our PDA than we had in banks of filing cabinets 30 years ago. But, smart companies realize that it is not beneficial to only collect the data, but it is important to get the right data and most important to take that data and transform it into the correct information that will allow processes to be improved,  hold employees accountable, and give management tools to make timely decisions and improve profits.

Using a baseball analogy (as I like to do) take a look at these two sets of data: 

• A:0,3,3,1,0,1,2,1,1,3,3,0,0,0,0,0,1,0,1,0,0,1,4,0,1,0,3,0,0,2,4,1,2,0
• B:1,0,2,0,0,0,1,0,4,0,2,1,0,0,0,4,0,0,0,2,0,0,0,1,0,0,0,0,0,0,2,0,0,0,5,0,2,0,0,0,1,2,0

The data above represents runs given up by game for two particular pitchers during a season.

So, now that we know that, let’s analyze the data and present some information:

• Pitcher A gave up 38 runs in 34 games
• Pitcher B gave up 30 runs in 43 games

Analyzing that information, we determine that:

• Picher A gave up 1.12 runs/game
• Pitcher B gave up 0.70 runs/game

Therefore we would assume based on this information that Pitcher B is better pitcher.

In further analyzing the data we find

• Pitcher A gave up 0 runs in 15 of 34 games (44%)
• Pitcher B gave up 0 runs in 29 of 43 games (68%)

So once again B is better

Ok, let’s add some data:

• A: 1,1,0,1,1,1,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,0,1,0,0,1
• B: 0,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,0,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,0,0,0,0,1,0,0

1 represents a win by the team and 0 represents a lose by the team for each game the pitcher was in.

• Pitchers A team won 24/34 games (71%)
• Pitcher B team won 31/43 games (72%)

Once again pitcher B compare favorably.

So having the above data will tell you that B is the better pitcher.

The following data is critical to getting the correct information for the pitchers’ analysis:

• A: 7,7,9,9,12,11,8,9.2,8,8,9,9,9,9,9,9,9,9,9,9,9,9,11,9,9,9,9,9,10,8,9,8,8,9
• B: 1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,.2,1.1,1,1,1,1,1,1,1.1,1.1,1,1,1.2,1.1,.1,.1,1.2,1, .2,1,2

The above data represents the number of innings each pitcher threw in each appearance. Therefore:

• Pitcher A threw 304.2 innings
• Pitcher B threw 45.2 innings

More importantly

• Pitcher A had an ERA of 1.12 (runs given up/9 innings pitched)
• Pitcher B had an ERA of 5.91

Pitcher A was Bob Gibson in 1968. His ERA that year was the 4th lowest of all time (every other season in the top 41 happened before 1920!).

Pitcher B was Keith Foulke, closer for the Boston Red Sox, in 2005.

As you can see in the above example, having data, but not the right information, can be very deceiving. It may even make you think that Keith Foulke was a better pitcher than Bob Gibson