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A Z2 Heavy Strategy: Trading Time For Adaptations

By David Brown

Bored Cyclist There's been a lot of discussion about Z2 training lately in the cycling press, a lot of it fueled by Z2 advocate Iniago St. Millian, who has coached the wildly successful Tadej Pogacar. But how viable is a Z2 heavy strategy for most cyclists? In the end, there are probably going to be as many answers as there are cyclists, depending on goals, training preferences and time constraints. This article explores my attempt to answer the question for myself, using a data-driven approach.

Training adaptations that lead to performance improvements are the goal for competitive cyclists. There's a table (Effect Size Table or EST), that can be found on a Training Peaks blog post authored by Dr. Andrew Coggan, titled "Expected Physiological and Performance Adaptations Resulting From Training in Zones 1-7". It lists 13 adaptations, along with an relative effect size, by zone. After a round of database normalization, I pulled this data into the KOMInformatics.com system database. Any mistakes incurred during this process are mine alone.


My initial step was, using the data in the EST, calculate what I'm calling adaptation load for every second of every ride in 2024. Much like the concept of training load, adaptation load doesn't map directly to any physical state in an athlete's body, it's just an abstract way of characterizing the effect of a training stimulus. The result of this step provides targets for each adaptation for Z2 training. How much Z2 training would I have to do to meet or exceed the adaptations accrued through my actual training distribution?

Adaptation load is defined as the kilojoules expended x effect size. Watts were calculated as a rolling 30 second average, mirroring the first step of the approach used to calculate weighted average power. kj = Watts/1000. I treated the EST effect size values as starting values, and the values from the next zone up as ending values. In most cases, the starting value represents a floor, and the ending value represents a ceiling, but not always. I looked at the watts value for each second, and calculated what percentage of the way the watt was to the ending value of the zone it belonged to. For instance, my Z2 zone starts at 157 watts and ends at 213 watts. If the watts value for a second was 185 this is exactly 50% of the way between 157 and 213. The EST has the Z2 effect size for Increased plasma volume as 1 which I'm treating as a floor, the ceiling is 2 (which is the effect size for this adaptation for Z3). Therefore, the adaptation load for this second for that particular adaptation would be (185/1000) * 1.5 or 0.2775.

This approach smooths out effect size and avoids implausible results that might accrue from taking the EST too literally. I find it hard to envision a world where riding at 156 watts has no training effect, but just 1 watt more has a full effect.

All Rides 2024 Results (1/1/2024 - 4/17/2024)

After implementing the above approach in SQL, I found I rode 164:41:2 for the date range, a little over 10 hours per week, with the following results for adaptation load:

Power Zone Distribution (1/1/2024 - 4/17/2024)
Zone Name Time In Zone
Z1 74:02:46
Z2 66:06:17
Z3 12:29:18
Z4 8:57:03
Z5 2:17:28
Z6 42:53
Z7 5:44

Adaptation Load (1/1/2024 - 4/17/2024)
  Adaptation Name Adaptation Load
1 Increased plasma volume 136360
2 Increased muscle mitochondrial enzymes 213126
3 Increased lactate threshold 212294
4 Increased muscle glycogen storage 223442
5 Hypertrophy of slow twitch muscle fiber 124029
6 Increased muscle capillarization 124029
7 Interconversion of fast twitch muscle fiber 203815
8 Increased stroke volume/maximal cardiac output 136360
9 Increased VO2Max 136360
10 Increased muscle high energy phosphate (ATP/PCr) stores 879
11 "Increased anaerobic capacity (""lactate tolerance"")" 9966
12 Hypertrophy of fast twitch muscle fibers 879
13 Increased neuromuscular power 954

Construction Of The Z2 Heavy Zone Distribution

The next step involved constructing the first Z2 heavy zone distribution scenario.

My initial thought was simply to move all of the non Z2 time from my actual zone distribution to and compare that. Upon reflection, I thought this approach wouldn't be realistic. My actual zone distribution contains a lot of Z1 time involving warmups, descents, deceleration for stops, and acceleration out of stops. As a practical matter, it would be pretty much impossible to avoid this time in zone. Therefore, any time increases which led to increases in adaptation load in any of the Z2 scenarios would involve time increases in both Z1 and Z2. The rule I used is every increase of Z2 time would also involve an increase of .813 of the Z2 value to Z1 time, mirroring the ratio between Z1 and non-Z1 time from my actual zone distribution. Each successive scenario involves an increase of 1 hour of Z2 training time compared to it's predecessor.

For wattages, I just used average watts based on Zone.

The initial scenario values are as follows:

Z2 Heavy Scenario 1 Watts/Time Values By Zone
Zone Watts Seconds
Z2 177 326323
Z1 112 266566

Here is the breakdown of how the Adaptation Load changed for each scenario. The load from each Z2 heavy scenario are expressed as a percentage of the load from the Actual zone distribution. In the initial scenario where hours are equal, the Actual zone distribution is yielding significantly more adaptation load than the the Z2 heavy scenario.

Adaptation Load Percentage Change Scenario
  Adaptation Name Scenario (Z2% of Actual)
    1 (164:41:2) 2 (193:42:4) 3 (222:43:5) 4 (251:45:0)
1 Increased plasma volume 73 86 99 112
2 Increased muscle mitochondrial enzymes 84 99 114 128
3 Increased lactate threshold 84 99 114 129
4 Increased muscle glycogen storage 89 105 121 137
5 Hypertrophy of slow twitch muscle fiber 80 95 109 123
6 Increased muscle capillarization 80 95 109 123
7 Interconversion of fast twitch muscle fiber 88 103 119 134
8 Increased stroke volume/maximal cardiac output 73 86 99 112
9 Increased VO2Max 73 86 99 112
10 Increased muscle high energy phosphate (ATP/PCr) stores 0 0 0 0
11 "Increased anaerobic capacity (""lactate tolerance"")" 0 0 0 0
12 Hypertrophy of fast twitch muscle fibers 0 0 0 0
13 Increased neuromuscular power 0 0 0 0

Here are the training hours for each scenario:

Scenario # Actual Training Time Z2 Heavy Hypothetical Training Time Weekly Time Difference
1 164:41:2 164:41:2 0:00
2 164:41:2 193:42:4 1:48:49
3 164:41:2 222:43:5 3:37:39
4 164:41:2 251:45:0 5:26:28

And here is the breakdown of training time for the actual, and each Z2 heavy scenario by power zone.

Power Zone Distribution (1/1/2024 - 4/17/2024)
Zone Name Time In Zone
Zone Name Actual Scenario 1 Scenario 2 Scenario 3 Scenario 4
Z1 74:02:46 74:02:46 87:03:58 100:05:1 113:06:2
Z2 66:06:17 90:38:43 106:38:4 122:38:4 138:38:4
Z3 12:29:18
Z4 8:57:03
Z5 2:17:28
Z6 42:53
Z7 5:44



A large point against any of the Z2 heavy strategies described above is that they miss 4 adapations (Increased muscle high energy phosphate (ATP/PCr) stores, Increased anaerobic capacity ("lactate tolerance"), Hypertrophy of fast twitch muscle fibers, Increased neuromuscular power) entirely. Any cycling discipline that involves sprinting or hard accelerations requires athletes to develop these adaptations in order to do well. Even time-trialing requires short-term accelerations off starts, turnarounds and up hills. Moreover, studies like this one show that work in ranges that elicit these adaptations also have a beneficial effect on aerobic adaptations. Also, at several junctures working with the KOMInformatics system for the last 5 years, Z6 interval work has shown, for me, a statistically significant correlation with PR's in the 8m-20m and 20m-1h categories.

Another point against a Z2 heavy strategy for me is the training time it would take to cover all of the adaptations from my actual training distribution. I'd have to up my weekly training to 15:44:3, about a 50% bump. That's a serious increase, the total hours are pretty much aligned with the hours incurred by many professionals. I would have to implement this strategy gradually, even a 1 hour increase per week would be pretty aggressive, if followed for more than 2 weeks. At 67, I feel I wouldn't be able to recover from this many hours on an ongoing basis. If I were willing to lose just 1% on 3 adaptations, Scenario 3, involving 13 hours 55 minutes 12 seconds of weekly training time might be a better way to go. I'd cover all the rest of the aerobic adaptations by at least 9%. This 40% bump is still a serious increase.

Another con is that it takes a surprising amount of self-discipline to stay at Z2 intensity.

Another con is that by training at low intensity all of the time, one won't build any knowledge about how harder efforts feel. A lot of hard interval work is psychological - being comfortable with being uncomfortable. Practicing intervals develops confidence in being able to perform intervals in race conditions.

Another con is that by training at low intensity all of the time one won't realize when Critical Power/FTP has changed. All of the reliable ways to test these numbers involve going hard.


If I somehow were able to follow the 15:44:3 Z2 heavy strategy to cover all of the adaptations from my actual training distribution, I would be at least 23% higher in 6 of the 9 aerobic adaptations, and 12% higher in the remaining 3. Those with the time to put in, and the ability to recover from it could reap substantial benefits from following a Z2 heavy strategy.


1. Andrew Coggan, Cycling Power Zones Explained

2. Fatma Rhibi et. al. Effects of different training intensities in high-intensity interval training (HIIT) on maximal aerobic velocity, hematological and muscle-damage markers in healthy young adults

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