Distance Training

DISTANCE TRAINING

By Paul Slovic

Paul Slovic, a psychologist at the Oregon Research Institute, had his stastical survey published in the October 1973 Runner's World.

On Feb 24, 1973, 541 runners started the Trail's End marathon at Seaside, Ore. In the preceding 54 days since the first of the year, this group had run more than 100,000 miles in preparation for the event. The runners' training programs were as varied as their backgrounds and abilities. Whereas some had run close to 900 miles during this time, averaging 17 per day, others had done virtually no training.

The casual, unprepared runner is atypical, however. Most marathon runners are quite concerned, if not obsessed with training. Although only a few of them entertain visions of finishing high in the standings, almost all have personal goals: to achieve a certain time, or perhaps just "finish the distance." Training proceeds with these goals in mind.

The amazing thing about this tremendous expenditure of time and effort is that most of it fashioned without the benefit of factual evidence. Intuition and hearsay, mixed with imitation and eventually modified by personal and sometimes painful experience, serve to shape the runner's program.

This study is a first step in the examination of the relationship between a runner's training program and his performance in the marathon. It reports the results of runners at Seaside, in which their answers to questions about their training were systematically related to their intermediate and final times in the run.

The Trail's End race is a particularly attractive setting for such a study for several reasons: It draws one of the largest fields of participants in the US, and the runners cover the entire range of experience and ability, from national and international class to novice.

The survey questionnaire was enclosed with the packet of materials distributed to each runner on the morning of the race. Questionnaires were returned at the post-race dinner and by mail. Out of 441 finishers, 178 men and six women returned the questionnaires. (There were not enough women respondents to warrant analyzing their replies separately, and their results were combined with those of the men. There were only a few returns from non-finishers and they were not analyzed.)

The survey questions provided over two dozen items of information about each runner. Table One presents some basic descriptive statistics on each of these items for the 184 individuals. Included are maximum and minimum values in the group, the average value, the median, and the 25th and 75th percentile values (denoted P-25 and P-75).

Table 1: Descriptive Statistics Of Entire Sample

One hundred eighty-four runners responded---178 men and six women. Only 148 of them reported a fastest mile time. Note that "ponderal index" is height in inches divided by cube root of weight, and is a measure of leanness (higher the value, leaner the individual). All training information, plus injury/illness statistics, apply to the January/February period.

	Minimum	P-25	Median	Average	P-75	Max
Final Time	2:20	2:57	3:24	3:28	4:03	5:20
Age	13	20	28	30	40	65
Height	4'10"	5'8"	5'10"	5'10"	6'0"	6'4"
Weight (pounds)	74	140	148	149	160	209
Ponderal Index	12.0	12.9	13.1	13.2	14.1	14.5
Years Running	0	2	4	5	7	24
Prior Completed Marathons	0	0	1.2	2.5	3.0	24
Miles Run (January)	0	100	170	181	237	510
Miles Run (February)	9	100	150	160	200	420
Miles Run (Jan. + Feb.)	9	215	328	340	433	890
Miles Run (week prior to race)	0	24	36	37	48	120
Maximum miles (one week)	9	45	62	63	77	133
Weeks since last Maximum Week	1	2	2.5	2.7	4	7
Longest Training Run	3	14	20	18	21	40
Runs Over 20 Miles	0	0	.9	1.7	2	10
Days Trained Per Week	0	5	6	6	7	7
Fastest Mile (past year)	7:00	5:40	5:07	5:16	4:48	4:12

Illness or injury	47%	Having A Coach	8%
Flu	19%	Self-coached	74%
Foot or leg injury	24%	Coached & Self-Coached	18%
First Marathon	35%	On H.S. or College Team	56%

Each of the 184 respondents was assigned to one of eight categories, according to his final time at Seaside. The implications:

Ponderal Index: Runners who finished under 3 1/2 hours were markedly leaner than those who finished after them. Those who finished above 4 3/4 hours had particularly low values on the ponderal index. The average heights in each of the eight groups were quite similar (three-fourth-inch range from the shortest to the tallest average), but average weights varied from 137.4 pounds in the 3:01-3:15 category to 163.8 pounds in the slowest category.
Prior Completed Marathons: The fastest runners had considerably more experience marathoning than did the slower runners. Fewer than 20% of those under three hours had not completed a marathon before, whereas over 90% of those in the slowest category had no prior completions. (The average high in the 3:31-3:45 category is primarily due to one runner who had completed 24 marathons.)
Miles Run: As one would expect, the fastest runners logged considerably more miles than the slower runners, regardless whether the period under consideration was a month, a week, or a single run. The slower runners ran a higher percentage of their total miles in February and had the maximum mileage week closer to the marathon than did the faster runners. Both of these results are indicative of a relatively late start in the training programs of the slower runners.
Training Days Per Week: There is no surprise here. The faster runners, who were covering much more ground in training, were also taking more training days to do it.
High School Or College Experience: Eighty-five percent of the runners who finished under 2:45 had experience on a college or high school track team. The percentages are lower for the other groups, and there is no systematic change from one time category to the next as final time increases. About half of the runners over 2:45 had running experience in high school or college.
Fastest Mile: It's also no surprise that the fastest marathon-runners had the fastest mile times in the past year. Of interest, however, is the generally fast level of times in all categories, and the fact that the average mile times in the two slowest categories were as fast as those in the 3:46-4:00 group.
Illness and Injury: Of particular interest is the finding that the percentage who reported that their training was interrupted by illness or injury did not differ systematically across the time categories.

Table 2: Averages By Time Category

Final Time (No. of Runners)

	2:20-	2:46-	3:01-	3:16-	3:31-	3:46-	4:01-	over
	2:45	3:00	3:15	3:30	3:45	4:00	4:15	4:30
	(26)	(33)	(16)	(31)	(19)	(26)	(17)	(16)
Ponderal Index	13.4	13.2	13.5	13.2	13.0	13.0	13.1	12.8
Completed Marathons	4.5	4.3	2.5	1.9	3.1	.8	1.2	.4
First Marathon	16%	12%	38%	29%	32%	65%	35%	81%
Miles Run (January)	300	252	175	147	179	122	96	98
Miles Run (February)	240	192	164	144	158	121	103	115
Miles Run (Jan. + Feb.)	540	444	339	290	337	242	199	212
Miles Run (week prior to race)	50	43	40	35	39	31	28	23
Maximum Miles (one week)	92	80	64	57	62	48	42	42
Weeks Since Maximum	3.2	3.4	2.8	2.2	2.9	2.5	2.0	2.2
Longest Training Run	22	22	20	18	19	16	14	13
Runs 20 Miles And Over	3.0	3.2	2.0	1.1	1.7	.5	.4	.2
Days Trained Per Week	6.5	6.2	6.2	5.5	5.5	5.3	4.4	4.8
Fastest Mile: Past Year	4:39	4:56	5:06	5:13	5:31	5:32	5:45	5:52
Illness or Injury	46%	48%	62%	45%	47%	42%	35%	56%

A statistical technique known as "correlational analysis" was used to generate equations to predict final time in the marathon. The best single predictor was the runner's fastest mile time in the past year. The equation was:

FT = .69X - 12.8

"FT" is the final time in minutes and "X" is fastest mile time in seconds. Thus, for a runner whose fastest mile was 300 seconds (5:00), the equation predicts a marathon time of 194.2 minutes (3:14:12). Another implication of this equation is that predicted final time decreases about 6.9 minutes for every 10 seconds' reduction in fastest mile time.

Correlational analysis was also used to develop equations that included more than one variable. These equations predicted more accurately than any equation having just one variable. Table Three presents the equation found to provide the best predictions of final time.

Part A of the table has equations that include fastest mile as a predictor. In addition to the fastest mile, previous marathon experience, mileage in the previous eight weeks, longest training runs, runs 20 miles and longer, and maximum mileage week were all-important predictors.

Since not all marathon runners have a recent mile time, equations in Part B purposely excluded this variable. When fastest mile is excluded, age and ponderal index also come into play as important.

Of particular interest is the finding that having completed a marathon is associated with a 14- (equation one) to 19-minute (equations six and eight) reduction in predicted final time, independent of the runner's training and ability. It may be that runners who have previously finished a marathon are motivated to improve their times rather than simply "getting through." Or perhaps the experience gives them confidence that the escalating discomfort can be endured.

The presence of longest run and runs 20 miles and longer in the equations that also included eight weeks' mileage and maximum-mileage week indicates that the more long runs taken, and the greater the length of the longest run, the faster the final time---independent of the total or maximum weekly mileage. In other words, longer runs would be associated with faster times even if total or weekly mileage were held constant.

Table 3: Equations To Predict Marathon Time

Abbreviations include: Mile - fastest mile time (in seconds) within the last year; Prev - previous completed marathon (if yes, multiply by one; if no, zero; do not multiply by number of completed marathons); 8 Wks - miles run in previous eight weeks; Long - longest run (miles) in the eight weeks; 20+ - number of runs 20 miles and more in the eight weeks; Max - most miles in one week during the eight weeks; PI - ponderal index; Age - age in years.

Note in the calculations of Part B that you're working with minus figures. For instance, -20 and -10 = -30, not -10.

Part A. Equations including Fastest Mile as a prediction variable:

1. FT = .51 (Mile) - 14.3 (Prev) - .05 (8 Wks) - 1.22 (Long) + 94.0

2. FT = .51 (Mile) - 15.7 (Prev) - .05 (8 Wks) - 2.86 (20+) + 75.6

3. FT = .51 (Mile) - 14.9 (Prev) - .27 (Max) - 1.34 (Long) + 95.0

4. FT = .51 (Mile) - 16.0 (Prev) - .31 (Max) - 3.31 (20+) + 80.2

Part B. Equations not including Fastest Mile as a predictor:

5. FT = -19.2 (PI) - 18.3 (Prev) + .7 (Age) - .07 (8 Wks) - 1.7 (Long) + 504

6. FT = -21.2 (PI) - 19.5 (Prev) + .7 (Age) - .07 (8 Wks) - 3.8 (20+) + 511

7. FT = -19.2 (PI) - 18.6 (Prev) + .7 (Age) - .5 (Max) - 1.4 (Long) + 507

8. FT = -20.7 (PI) - 19.0 (Prev) + .7 (Age) - .5 (Max) - 3.7 (20+) + 511

Using equation one for runners who finished in less than four hours, predicted times and actual times differed by an average of 15.3 minutes. Runners finishing in more than four hours were less predictable. The average error of prediction for them was 32.2 minutes.

The results of this study indicate that equations can be constructed to predict performance in the marathon from knowledge of personal characteristics and training specifics. These equations predict moderately well, but there are nevertheless frequent and large deviations from predicted and actual performances.

Generalizations from these results should be made with caution, for several reasons. First, the results may be specific to the particular respondents to this survey, who tend to be older and faster than the non-respondents. Also, non-finishers were not included in the results. And the results may be somewhat specific to the particular marathon course, and the weather on race day and during the preceding two months.

One way to test the general application of the results would be to repeat the survey on another group of marathon runners. This should be done. If it is, questions about mood and feeling during and after the race, and about diet would make an interesting addition to the survey.