I’m reaching out to colleagues involved in continuous improvement and I’m looking for high quality content for our new online publication titled, Quality Focus Magazine (http://www.QualityFocusMag.com)
Looking for a broad range Quality related topics.
Subjects dealing with metrics, process improvement methodologies, case studies, challenges, deployment, statistics… you get the idea. We would also love to see unique and different topics; e.g., humor, speculation, tools, Opinions, or articles on Quality Management in someone’s personal life.
We are looking for good content, not necessarily new content. If you’ve been considering renovating a 2008 article on Muda, Muri, and Muni for a 2014 audience, we’d love to work with you on it. If you are a blogger, consider putting some of your posts together into a longer article. Bottom line is that we want to provide interesting insights to our readers around the world. We’re looking for between 500-2000 words. We LOVE content with visual elements. We work hard to leverage the digital magazine where images really pop!
- You will be published Quality Focus Magazine in 85 countries through the Apple iTunes Newsstand
- Your article will also be available for PDF download
- Your article is accompanied by your bio w/ picture
- You retain control of your article once it has been published.
Are You interested and ready to go!?
Great! Our monthly deadline is the 15th of the month, so we work on tight timelines. The deadline for the March issue is February 16th, so if you have something in mind, by all means, send it along soon!
Are You interested but still have questions?
That’s fine too. We want to form long term relationships with industry insiders who have interesting points of view. Even if you don’t have a specific topic in mind, we would love to hear from you. We can help you select a great topic and/or help you write your submission. If you’d like to see an example of a recent issue, please send me a note and I’ll provide a link.
You can respond by replying through LinkedIn, or by emailing me at: email@example.com
I look forward to hearing from you.
WordPress is an outstanding tool… this is just one more example.
The WordPress.com stats helper monkeys prepared a 2013 annual report for this blog.
Here’s an excerpt:
The concert hall at the Sydney Opera House holds 2,700 people. This blog was viewed about 17,000 times in 2013. If it were a concert at Sydney Opera House, it would take about 6 sold-out performances for that many people to see it.
The University of Central Florida beat Baylor University in the 2014 Fiesta Bowl. SB Nation declared this victory, “the biggest BCS bowl upset ever”. I watched the game, and from the opening kickoff, it was clear that these two teams were equally matched. I love sports, and I especially enjoy American Football.
Why did the pundits consider UCF’s victory an upset?
#1: Experts discounted UCF’s 2013 accomplishments based on their schedule.
UCF plays in the American Athletic Conference, not in a ‘power conference’ like the SEC or the PAC 12. They don’t even play in a former power conference like the Big 10 or Big 12.
#2: UCF does not have a rich and storied football tradition.
According to wikipedia, the University of Central Florida opened in 1968 as Florida Technological University, with the mission of providing personnel to support the growing U.S. space program at the Kennedy Space Center, which is located only 35 miles (56 km) to the east.
(or, “nobody else is saying that UCF has a chance to beat Baylor, so I’m not going to the first on on the UCF bandwagon”)
Groupthink is the practice of thinking or making decisions as a group in a way that discourages creativity or individual responsibility. American football comentators fall into two categories: jocks and journalists. There are two subcategories of jocks, the players, and the coaches. Some, like the legendary Mike Ditka, were renowned as coaches and players. But when it comes to football expertise, there is a rigid hierarchy, jocks hold the trump card in every argument, coaches trump the journalists, and nearly to a man, everyone in this arena believes the ‘stats guy’ is a subhuman parasite who is hell bent on wrecking the noble game. Despite the popularity of Moneyball, and the success of the baseball teams using its principles, we continued to be fooled by what we see. Some people are willing to look past what they see to the facts. One of those people is Josh Friemel.
Josh Friemel Nailed IT… with FACTS on 12/31/2013!
I had never read Josh Friemel’s work until today, but I am impressed with his “pre-game” analysis of the Baylor vs. University of Central Florida Fiesta Bowl game. Josh writes for the Dallas Morning News’ sports blog… and in his December 31, 2013 post titled, What’s up with UCF? Five things Baylor fans should know about the Knights, he lays out a convincing case for UCF. He talks about their strong defense, how their offense is balanced, the passing prowess of UCF quarterback Blake Bortles and about the Knights’ lone loss to the University of South Carolina. He backs up each assertion with facts.
But he still predicted that Baylor would win easily…
Given time to reflect on the things Baylor fans needed to know about UCF must have lessened his zeal for the Knights since at lunchtime on New Year’s Day, Josh posted, Prediction: UCF can’t handle Baylor’s speed, Bears easily win first ever BCS bowl. Please don’t think I’m being hard on Josh. He was the ONLY person I could find in who had published anything remotely positive related to the UCF Knight team’s chance to beat Baylor.
What can we learn?
Guard Against Groupthink.
In this case, former players and coaches spout opinions as if they are facts, and poo poo anyone who disagrees. In Howard Cosell’s book “I Never Played the Game” he helped popularize the term “jockocracy” and he talked openly about announcing jobs being given to former atheletes who had not earned them. Ironically, Cosell was replaced for the 1985 World Series broadcast by former St. Louis Cardinal Tim McCarver. <I’ll refrain from opining on Mr. McCarver’s journalistic chops in this post.>
Learn About “Confirmation Bias”
Confirmation bias (also called confirmatory bias or myside bias) is the tendency of people to favor information that confirms their beliefs. This is a difficult problem to deal with, since most of us are unaware of our biases, or worse, we are convinced that our biases are facts.
I strongly recommend Chip and Dan Heath’s book, Decisive: How to Make Better Choices in Life and Work. It’s a great look at how we make decisions and how we can improve as individuals and as organizations.
Bob Hubbard, 1/3/2014
To the detriment of most companies and organization, too many are focused on the NPS score. As a consequence, they often miss the mark. The “Net” in NPS is too far from the bone, as it were, and so it makes sense that the mark is often missed by organizations.
What is needed is an understanding of the component and atomic parts of NPS. That takes us closer to the bone and the heart of the Customer Experience.
Net Promoter Score
For review, NPS is calculated by taking the % of Promoters Less the % of Detractors (img src: Satmetrix).
As you can see, the value of NPS is that it is statistically very difficult to obtain promoters – there’s only a 2/11 chance of obtaining a promoter. With 7/11 of the weight slanted toward detractors, it forces the enterprise to listen and focus it’s efforts on reducing the root causes of detractors.
from HBR.org Blog
Know the Difference Between Your Data and Your Metrics
by Jeff Bladt and Bob Filbin | 11:00 AM March 4, 2013
How many views make a YouTube video a success? How about 1.5 million? That’s how many views a video our organization, DoSomething.org, posted in 2011 got. It featured some well-known YouTube celebrities, who asked young people to donate their used sports equipment to youth in need. It was twice as popular as any video Dosomething.org had posted to date. Success! Then came the data report: only eight viewers had signed up to donate equipment, and zero actually donated.
Zero donations. From 1.5 million views. Suddenly, it was clear that for DoSomething.org, views did not equal success. In terms of donations, the video was a complete failure.
What happened? We were concerned with the wrong metric. A metric contains a single type of data, e.g., video views or equipment donations. A successful organization can only measure so many things well and what it measures ties to its definition of success. For DoSomething.org, that’s social change. In the case above, success meant donations, not video views. As we learned, there is a difference between numbers and numbers that matter. This is what separates data from metrics.
You can’t pick your data, but you must pick your metrics.
Take baseball. Every team has the same definition of success — winning the World Series. This requires one main asset: good players. But what makes a player good? In baseball, teams used to answer this question with a handful of simple metrics like batting average and runs batted in (RBIs). Then came the statisticians (remember Moneyball?). New metrics provided teams with the ability to slice their data in new ways, find better ways of defining good players, and thus win more games.
Keep in mind that all metrics are proxies for what ultimately matters (in the case of baseball, a combination of championships and profitability), but some are better than others. The data of the game has never changed — there are still RBIs and batting averages; what has changed is how we look at the data. And those teams that slice the data in smarter ways are able to find good players that have been traditionally undervalued.
Organizations become their metrics.
Metrics are what you measure. And what you measure is what you manage to. In baseball, a critical question is how effective is a player when he steps up to the plate? One measure is hits. A better measure turns out to be the sabermetric “OPS” — a combination of on-base percentage (which includes hits and walks) and total bases (slugging). Teams that look only at hitting suffer. Players on these teams walk less, with no offsetting gains in hits. In short, players play to the metrics their management values, even at the cost of the team.
The same happens in workplaces. Measure YouTube views? Your employees will strive for more and more views. Measure downloads of a product? You’ll get more of that. But if your actual goal is to boost sales or acquire members, better measures might be return-on-investment (ROI), on-site conversion, or retention. Do people who download the product keep using it, or share it with others? If not, all the downloads in the world won’t help your business.
In the business world, we talk about the difference between vanity metrics and meaningful metrics. Vanity metrics are like dandelions – they might look pretty, but to most of us, they’re weeds, using up resources, and doing nothing for your property value. Vanity metrics for your organization might include website visitors per month, Twitter followers, Facebook fans, and media impressions. Here’s the thing: if these numbers go up, it might drive up sales of your product. But can you prove it? If yes, great. Measure away. But if you can’t, they aren’t valuable.
Metrics are only valuable if you can manage to them.
Good metrics have three key attributes: their data are consistent, cheap, and quick to collect. A simple rule of thumb: if you can’t measure results within a week for free (and if you can’t replicate the process), then you’re prioritizing the wrong ones. There are exceptions, but they are rare. In baseball, the metrics an organization uses to measure a successful plate appearance will impact player strategy in the short term (do they draw more walks, prioritize home runs, etc.?) and personnel strategy in the mid and long terms. The data to make these decisions is readily available and continuously updated.
Organizations can’t control their data, but they do control what they care about. If our metric on the YouTube video had been views, we would have called it a huge success. In fact, we wrote it off as a massive failure. Does that mean no more videos? Not necessarily, but for now, we’ll be spending our resources elsewhere, collecting data on metrics that matter. Good data scientists know that analyzing the data is the easy part. The hard part is deciding what data matters.
An Introduction to Sabermetrics
by Jim Albert
What is Sabermetrics?
Sabermetrics is the mathematical and statistical analysis of baseball records. To understand the field of Sabermetrics, one first should be familiar with the game of baseball. This sport is one of the most popular games in the United States; it is often called the national pastime. Baseball began in the eastern United States in the mid-1800′s. Professional baseball started near the end of the 18th century; the National League was founded in 1876 and the American League in 1900. Currently in the United States, there are 28 professional teams in the American and National Leagues and millions of people watch games in ballparks and on television.
The game of baseball
The game of baseball is played between two teams, each consisting of nine players. The nine players are a pitcher, a catcher, first baseman, second baseman, shortstop, third baseman, left fielder, center fielder and right fielder. A game of baseball consists of nine innings. One inning is divided into two halves; in the top half of the inning, one team plays in the field and the second team comes to bat, and in the bottom half, the teams reverse roles. The team that is batting during a particular half-inning is trying to score runs. The team with the higher number of runs at the end of the nine innings is the winner of the game.
During an inning, a player on the team in the field, called a pitcher, throws a baseball toward a player of the team at-bat, called the batter. The batter will try to hit the ball using a wooden stick (called a bat) in a location out of the reach of the players in the field. By hitting the ball, the batter has the opportunity to run around four bases that lie in the field. If a player advances around all of the bases, he has scored a run. If a batter hits a ball that can be caught, or that can be thrown to first base before he runs to that base, then he is said to be out, and cannot score a run.
A batter is also out if he fails to hit the baseball three times or if three good pitches (called strikes) have been thrown.
The objective for the batting team during an inning is to score as many runs as possible before obtaining three outs.
The basic batting statistics
One notable aspect of the game of baseball is the wealth of numerical information that is recorded about the game.
The effectiveness of batters and pitchers is typically assessed by particular numerical measures. The usual measure of hitting effectiveness for a player is the batting average which is computed by dividing the number of hits by the number of at-bats. This statistic gives the proportion of opportunities (at-bats) in which the batter succeeds (gets a hit). The batter with the highest batting average during a baseball season is called the best hitter that year. Batters are also evaluated on their ability to reach one, two, three, or four bases on a single hit; these hits are called respectively singles, doubles, triples, and home runs. The slugging average is computed by dividing the total number of bases (in short, total bases) by the number of opportunities. Since it weights hits by the number of bases reached, this measure reflects the ability of a batter to hit a long ball for distance. The most valued hit in baseball is the home run where a player advances four bases on one hit. The number of home runs is recorded for all players and the batter with the largest number of home runs at the end of the season is given special recognition.
The basic pitching statistics
A number of statistics are also used in the evaluation of pitchers. For a particular pitcher, one counts the number of games in which he was declared the winner or loser and the number of runs allowed. Pitchers are usually rated in terms of the average number of “earned” runs allowed for a nine inning game. Other statistics are useful in understanding pitching ability. A pitcher records a strikeout when the batter fails to hit the ball in the field and records a walk when he throws four inaccurate pitches (balls) to the batter. A pitcher who can throw the ball very fast can record a high number of strikeouts. A pitcher who is “wild” or relatively inaccurate will record a large number of walks.
Better measure of hitting ability — runs created
One goal of sabermetrics is to find good measures of hitting and pitching performance. Bill James (1982) compares the batting records of two players, Johnny Pesky and Dick Stuart, who played in the 1960′s. Pesky was a batter who hit for a high batting average but hit few home runs. Stuart, in contrast, had a modest batting average, but hit a high number of home runs. Who was the more valuable hitter? James argues that a hitter should be evaluated by his ability to create runs for his team. From an empirical study of a large collection of team hitting data, he established the following formula for predicting the number of runs scored in a season based on the number of hits, walks, at-bats,
and total bases recorded in a season.
(HITS + WALKS) (TOTAL BASES) RUNS = ---------------------------- AT-BATS + WALKS
This formula reflects two important aspects in scoring runs in baseball. The number of hits and walks of a team reflects the team’s ability to get runners on base. The number of total bases of a team reflects the team’s ability to move runners that are already on base. This runs created formula can be used at an individual level to compute the number of runs that a player creates for his team. In 1942, Johnny Pesky had 620 at-bats, 205 hits, 42 walks, and 258 total bases; using the formula, he created 96 runs for his team. Dick Stuart in 1960 had 532 at-bats with 160 at-bats, 34 walks, and 309 total bases for 106 runs created. The conclusion is that Stuart in 1960 was a slightly better hitter than Pesky in 1942 since he created a few more runs for his team.
An alternative approach to evaluating batting performance is based on a linear weights formula. George Lindsey (1963) was the first person to assign run values to each event that could occur while a team was batting. By the use of recorded data from baseball games and probability theory, he developed the formula
RUNS = (.41) 1B + (.82) 2B + (1.06) 3B + (1.42) HR
where 1B, 2B, 3B, and HR are respectively the number of singles, doubles, triples, and home runs hit in a game. One notable aspect of this formula is that it recognizes that a batter creates a run three ways. There is a direct run potential when a batter gets a hit and gets on base. In addition, the batter can advance runners that are already on base. Also, by not getting an out, the hitter allows a new batter a chance of getting a hit, and this produces an indirect run potential. Thorn and Palmer (1993) present a more sophisticated version of the linear weights formula which predicts the number of runs produced by an average baseball team based on all of the offensive events recorded during the game. Like James’ runs created formula, the linear weights rule can be used to evaluate a player’s batting performance.
Runs to wins
Although scoring runs is important in baseball, the basic objective is for a team to score more runs than its opponent. To learn about the relationship between runs scored and the number of wins, James (1982) looked at the number of runs produced, the number of runs allowed, the number of wins and the number of losses during a season for a large number of recent major league teams. James noted that the ratio of a team’s wins to losses was approximately equal to the square of the ratio of runs scored to the runs allowed. Equivalently,
WINS RUNS^2 RUNS = -------------- = --------------------------- . WINS + LOSSES RUNS^2 + OPPOSITION RUNS^2
This relationship can be used to measure a batter’s performance in terms of the number of wins that he creates for his team.
Better measure of pitching ability
Sabermetrics has also developed better ways of evaluating pitching ability. The standard pitching statistics, the number of wins and the earned runs per game (ERA) are flawed. The number of wins of a pitcher can just reflect the
fact that he pitches for a good offensive (run scoring) team. The ERA does measure the rate of a pitcher’s efficiency, but it does not tell you about the actual benefit of this pitcher over an entire season. Thorn and Palmer (1993) developed the pitching runs formula
League ERA PITCHING RUNS = Innings Pitched x ----------- - ER. 9
The factor (League ERA/9) measures the average runs allowed per inning for all teams in the league. This value is multiplied by the number of innings pitched by that pitcher — this product represents the number of runs that pitcher would allow over the season if he was average. Last, one subtracts the actual earned runs (ER) the pitcher allowed for that season. If the pitching runs is larger than 0, then this pitcher is better than average. This new measure appears to be useful in measuring the efficiency and durability of a pitcher.
Player game percentage
Good measures of hitting, pitching, and fielding performance of baseball players have been developed.
However, these statistics do not directly measure a player’s contribution to a win for his team. Bennett and Flueck (1984) used data from two baseball seasons to estimate the probability the home team wins a game given the run differential (the home team runs minus visiting team runs), the half inning (top or bottom of the inning), the number of outs, and the on-base situation. Using these estimated probabilities, one can see how the probability of
winning changes for each game event. One can measure a player’s contribution to winning a game by summing the changes in win probabilities for each play in which the player has participated. This statistic, called the Player
Game Percentage, was used by Bennett (1993) to evaluate the batting performance of Joe Jackson. This player was banished from baseball for allegedly throwing the 1919 World Series. A statistical analysis using the Player Game Percentage showed that Jackson played to his full potential during this series.
People are often interested in comparing batters or pitchers from different eras. In making these comparisons, it is important to view batting or pitching statistics in the context in which they were achieved. For example, Bill Terry led the National League in 1930 with a batting average of .401, a mark that has been surpassed since by only one hitter. In 1968 Carl Yastrzemski led the American League in hitting with an average of .301. It appears on the surface that Terry was the clearly superior hitter. However, when viewed relative to the hitters that played during the same time,
both hitters were approximately 27 percent better than the average hitter (Thorn and Palmer, 1993). The hitting accomplishments of Terry in 1930 and Yastrzemski in 1968 were actually very similar. Likewise, there are significant differences in hitting in different ball parks, and hitting statistics need to be adjusted for the ball park played to make accurate comparisons between players.
Learning from selected data
Watching a baseball game raises questions that motivate interesting statistical analyses. During the broadcast of a game, a baseball announcer will typically report selected hitting data for a player. For example, it may be reported that Barry Bonds has 10 hits in his most recent 20 at-bats. What have you learned about Bonds’ batting average on the basis of this information? Clearly, Bonds’ batting average can’t be as large as 10/20 = .500 since this data was chosen to maximize the reported percentage. Casella and Berger (1994) construct the likelihood function for a player’s true batting average on the basis of this selected information and find the maximum likelihood estimate. They conclude that this selected data only provides a little insight into the “complete data” batting average that is obtained from batting records over the entire season.
Another interesting question is on the existence of streakiness in hitting data. During a season it is observed that some ballplayers will experience periods of “hot” hitting where they will get a high proportion of hits. Other hitters will go through slumps or periods of hitting with very few hits. But these periods of hot and cold hitting may be just a reflection of the natural variability observed in coin tossing. Is there statistical evidence for a “hot hand” among baseball hitters where the probability of obtain a hit is dependent on recent at-bats? Albright (1993) looked at a large collection of baseball hitting data and used a number of statistics such as the number of runs to detect streakiness in hitting data. His main conclusion was that there little statistical evidence generally for a hot hand in baseball hitting.
Currently there is great interest among fans and the media in situational baseball data. The hitting performance of batters is recorded for a number of different situations, such as day versus night games, on grass fields and artificial turf fields, against pitchers who throw right-handed and left-handed, and during home and away games. There are two basic questions in the statistical analysis of this type of data. First, are there particular situations that can explain a significant amount of variation in the hitting data? Second, are there ballplayers that perform particularly well or poorly in a given situation? Albert (1994) analyzed a large body of published situational data and used Bayesian hierarchical models to combine data from a large group of players. His basic conclusion is that there do exist some important situations. For example, batters hit on average 20 points higher when facing a pitcher of the opposite arm, and hit 8 points higher when they are playing in their home ballpark. However, there is generally little statistical evidence for individual differences in these situational effects.
Major league baseball is currently divided into six divisions and one goal of any team is to finish first in its division. Suppose that part of the season has been completed. Using the teams’ records from this partial season, is it possible to predict accurately the winners of the divisions? Barry and Hartigan (1993) use a choice model for the probability that a team wins an individual game. This model allows for different strengths between the teams, different home advantages, and team strengths that can change randomly with time. The authors use this model to simulate the results of future baseball games and estimate the probabilities that each team will win its respective divisions.
Currently, major league baseball games are recorded in very fine detail. Information about every single ball pitched, fielded and hit during a game are noted, creating a large database of baseball statistics. This database is used in a number of ways. Public relations departments of teams use the data to publish special statistics about their players. The statistics are used to help determine the salaries of major league ballplayers. Specifically, statistical information is used as evidence in salary arbitration, a legal proceeding which sets salaries. A number of teams have employed full-time professional statistical analysts and some managers use statistical information in deciding on strategy during a game. Bill James and other baseball statisticians have shown that it is possible to answer a variety of questions about the game of baseball by means of statistical analyses.
- Albert, J. (1994), “`Exploring baseball hitting data: what about those breakdown statistics?”, Journal of the American Statistical Association , 89, 1066-1074.
- Albright, S. C. (1993), “A statistical analysis of hitting streaks in baseball,” Journal of the American Statistical Association , 88, 1175-1183.
- Barry, D., and Hartigan, J. A. (1993), “Choice Models for Predicting Divisional Winners in Major League Baseball,” Journal of the American Statistical Association , 88, 766-774.
- Bennett, J. M. (1993), “Did Shoeless Joe Jackson Throw the 1919 World Series?”, The American Statistician, 47, 241-250.
- Bennett, J. M. and Flueck, J. A. (1984), “Player Game Percentage”, in Proceedings of the Social Statistics Section, American Statistical Association, 378-380.
- Casella, G. and Berger, R. (1993), “Estimation With Selected Binomial Information or Do You Really believe that Dave Winfield is Batting .471?”, Journal of the American Statistical Association , 89, 1080-1090.
- James, B. (1982), The Bill James Baseball Abstract, New York: Ballantine Books. Lindsey, G. (1963) “An Investigation of Strategies in Baseball,”
- Operations Research, 11, 447-501.
- Thorn, J. and Palmer, P. (1993), Total Baseball, New York: Harper Collins.