100 kg for 5 reps points to a one-rep max of about 114.8 kg (253.1 lb); the four equations range from 112.5 to 117.5 kg.
Estimate by equation (kg)
Training loads from your estimated 1RM 行数:11
% of 1RM
kg
lb
Reps (Epley)
100 %
114.8
253.1
1
95 %
109
240.4
2
90 %
103.3
227.7
3
85 %
97.6
215.1
5
80 %
91.8
202.4
8
75 %
86.1
189.8
10
70 %
80.3
177.1
13
65 %
74.6
164.5
16
60 %
68.9
151.8
20
55 %
63.1
139.2
25
50 %
57.4
126.5
30
计算方法 S
Epley
100(1+305)=116.7
Brzycki
100×37−536=112.5
Lombardi
100×50.10=117.5
O'Conner
100(1+0.025×5)=112.5
Mean of the four
4116.7+112.5+117.5+112.5=114.8kg
Loads are in kilograms; pounds are converted at 1 lb = 0.45359237 kg.
关于One-rep max calculator
A one-rep max (1RM) is the heaviest load you can lift once with good form. It is estimated here from a set taken to failure with four equations: Epley, weight × (1 + reps ÷ 30); Brzycki, weight × 36 ÷ (37 − reps); Lombardi, weight × reps^0.10; and O'Conner, weight × (1 + 0.025 × reps). The main result is their mean, shown in kilograms and pounds.
Lifters use it to set training loads without a maximal attempt. With the default 100 kg for 5 reps, the four equations give 112.5 to 117.5 kg and a mean of 114.8 kg; the table then lists loads from 50 % to 100 % of that, with the reps Epley predicts at each.
The equations were fitted to groups of lifters and lose accuracy as reps rise, especially past 10. They are lift-specific, so an estimate from the bench press says nothing about the squat.
计算示例
100 kg for 5 reps
Weight lifted
100 kg
Reps completed
5
Epley
116.7 kg
Brzycki
112.5 kg
Lombardi
117.5 kg
O'Conner
112.5 kg
Estimated one-rep max (mean of four)
114.8 kg
核验来源:Python 3.8 decimal/math: 100(1 + 5/30), 100·36/32, 100·5^0.1, 100(1 + 0.125); mean of the four
核验来源:Convention stated in the steps: r = 1 returns the lifted weight for every equation (Brzycki and Lombardi give it exactly)
60 kg for 12 reps
Weight lifted
60 kg
Reps completed
12
Epley
84.0 kg
Brzycki
86.4 kg
Lombardi
76.9 kg
O'Conner
78.0 kg
Estimated one-rep max (mean of four)
81.3 kg
核验来源:Python 3.8 decimal/math: 60·1.4, 60·36/25, 60·12^0.1, 60·1.3; mean 81.3313
常见问题
How do you calculate your one-rep max?
Lift a weight for as many clean reps as possible in one set, then apply an equation. Epley's is weight × (1 + reps ÷ 30), so 100 kg for 5 reps gives 116.7 kg; Brzycki's is weight × 36 ÷ (37 − reps), giving 112.5 kg. This calculator averages Epley, Brzycki, Lombardi and O'Conner, which gives 114.8 kg for that set.
Which one-rep max formula is most accurate?
None is best for every lift or lifter, and they agree most at low reps. For 100 kg lifted 5 times the four span 112.5 to 117.5 kg, a 5 kg spread; for 60 kg lifted 12 times they span 76.9 to 86.4 kg, almost 10 kg. Estimates from sets of 10 reps or fewer are therefore more consistent, and the mean used here sits between the highest and lowest equation.
How many reps can you do at 80 % of your one-rep max?
About 8, by the Epley equation solved for reps: 30 × (100 ÷ 80 − 1) = 7.5, rounded to 8. The table under the result lists loads and Epley rep estimates from 50 % to 100 %: at 90 % it gives 3 reps and at 70 % it gives 13. Real rep counts at a given percentage vary between lifts and between lifters.
“One-rep max calculator”有多准确?
准确性取决于输入值和方法的假设。十进制运算使用50位有效数字,但估算、数值方法和源数据的精度可能较低;显示时的舍入并不能消除这些限制。 已按独立来源核验的计算示例:4。 例如,“100 kg for 5 reps”根据Python 3.8 decimal/math: 100(1 + 5/30), 100·36/32, 100·5^0.1, 100(1 + 0.125); mean of the four进行核验。
这种方法出自哪里?
LeSuer DA, McCormick JH, Mayhew JL, Wasserstein RL, Arnold MD. The accuracy of prediction equations for estimating 1-RM performance in the bench press, squat, and deadlift. J Strength Cond Res 1997;11(4):211–213; Brzycki M. Strength testing — predicting a one-rep max from reps-to-fatigue. J Phys Educ Recreat Dance 1993;64(1):88–90; Epley B. Poundage chart. Boyd Epley Workout. Lincoln, NE: Body Enterprises; 1985.
LeSuer DA, McCormick JH, Mayhew JL, Wasserstein RL, Arnold MD. The accuracy of prediction equations for estimating 1-RM performance in the bench press, squat, and deadlift. J Strength Cond Res 1997;11(4):211–213