# Keeping score

### Here’s a closer look at the stats that frequently pop up in this newsletter

I probably should have done this two years ago, back when I launched *Baseball’s Best (and Worst),* but I didn’t.

So — better late than never — what follows is my explanation of the unusual statistics that pop up from time to time in this newsletter.

My aim is to create a one-stop center for information about such arcane abbreviations as BPO, CT, SC, and TS. I’ll link back whenever one of my future stories mentions any of these stats. And if other obscure statistics elbow their way into my copy, I’ll circle back to add them to this directory.

The benefits, I think, are obvious. No longer will I need to clog a series of stories with repeated explanations of the same concept. And no longer will you need to wade through the attendant math — unless, of course, you find that kind of thing interesting.

So let’s get to the entries, which are in alphabetical order.

#### Subscribe — free — to Baseball’s Best (and Worst)

A new installment will arrive in your email each Tuesday and Friday morning

### Bases per out (BPO)

This is the ratio of the bases a batter reaches for every out that he makes. (BPO has a defensive side, too. The same formula can determine the ratio of bases a pitcher allows for every out that he obtains.) The formula: (1) Add up the bases reached (or allowed) through singles, doubles, triples, home runs, walks, hit batsmen, stolen bases, sacrifice hits, and sacrifice flies. (2) Determine the number of outs by subtracting hits from at-bats, and then adding double plays, caught stealings, sacrifice hits, and sacrifice flies. (3) Divide the number from Step 1 (bases) by the number from Step 2 (outs).

### Base value (BV)

This is the difference between the number of bases a batter reaches (or a pitcher allows) and the leaguewide average. The formula: (1) Multiply a player’s number of outs by the leaguewide BPO, thereby determining the number of bases a typical batter would have reached or a typical pitcher would have allowed. (2) Take the number from Step 1 (leaguewide average bases) and subtract it from the player’s number of bases. A positive BV is good news for a batter, indicating that he reached more bases than the typical player would have, while a negative BV signals a deficiency. The scales are reversed for a pitcher, who would prefer a negative BV.

### Batting eye rate (EY)

This is a measure of a batter’s plate discipline, as indicated by his ability to draw walks. The formula: (1) Subtract intentional walks from total walks. (2) Subtract intentional walks from plate appearances. (3) Divide the number from Step 1 (unintentional walks) by the number from Step 2 (plate appearances minus intentional walks).

### Contact rate (CT)

This average reflects a hitter’s ability to put the bat on the ball. The formula: (1) Subtract strikeouts from at-bats. (2) Divide the number from Step 1 (at-bats minus strikeouts) by at-bats.

### Fan support index (FSI)

This stat shows the relative level of fan enthusiasm that a franchise inspires. An FSI of 100 indicates support that is commensurate with a team’s quality on the field. The formula: (1) Determine a team’s attendance per home date. (2) Divide the number from Step 1 (average home attendance) by the club’s number of wins, both home and away. (3) Divide the number from Step 2 (team’s attendance/win ratio) by the average attendance/win ratio for all major-league teams in the same season. (4) Multiply the result by 100.

### Isolated power average (ISO)

This is a measure of a batter’s power, specifically his ability to produce extra-base hits. The formula: (1) Add a batter’s extra bases, with one for each double, two for each triple, and three for each home run. (2) Divide the player’s total of extra bases by his at-bats. The same stat can be calculated for the extra bases yielded by pitchers.

### Scoring (SC)

This is the count of runs directly generated by a player, similar to the scoring race in hockey. The formula: (1) Add runs and runs batted in. (2) Subtract home runs from that total (since they’re double-counted as runs and RBIs). Sabermetricians will hate this stat. They will say — correctly — that it doesn’t give credit to everybody who helps to produce a run. Consider this example involving the 1961 Yankees: Tony Kubek gets hit by a pitch, Roger Maris doubles him to third base, and Mickey Mantle drives in the run with an uncharacteristically weak infield squib. Both Kubek and Mantle get credit in the SC column, while Maris, the man who made the key hit, gets nothing. That’s unfortunate, but there are parallels in most sports. Football, basketball, and hockey statisticians tally touchdowns, baskets, and goals respectively, without concern for the lineman who makes a key block, the forward who zips an effective outlet pass, or the goalie who triggers a breakaway.

### Scoring efficiency rate (SE)

This is a matter of simple division. Take a player’s scoring total (SC) and divide it by his plate appearances.

### Team score (TS)

This is a measure of the relative excellence of a given team in a given season, as expressed on a 100-point scale. The formula gives equal weight to four factors: winning percentage, the differential between runs scored and allowed per game, the differential between BPO and BPOA (BPO allowed), and postseason success. The score for each of the first three factors is based on a comparison of the team’s number and the respective category’s leaguewide average and standard deviation for that year. The result (known as a z-score) is added to 2.75, multiplied by 25, and divided by 5.5. These calculations yield a score between 0 and 25 for each of the three factors. The fourth category gives 25 points to any World Series champion and 20 points to any league champion that lost the World Series. Any other team receives a bonus from 0 to 15 points after .250 is subtracted from its winning percentage and the resulting number is multiplied by 32.2. The four category scores are added to yield the team score (TS), which I consider to be an excellent indicator of a team’s relative strength. There are good reasons for all of the numerical machinations that I described in this paragraph, but I see no reason to go into greater detail. This entry is already long enough, don’t you think?