A spreadsheet for jump Force-Velocity-Power profiling

Written with Dr Pierre Samozino (University Savoie Mont Blanc)
Due to popular demand and frequent requests from sport practitioners and researchers, we have decided to publish a spreadsheet and tutorial for implementing our jumping FVP field method based on jump height measurements during loaded squat jumps. This simple method has been initially proposed and validated against force plate by Samozino et al. in 2008, and afterwards adapted to countermovement jump by Jimenez-Reyes et al., and recently to bench press by Rahmani et al.
Download this spreadsheet here: (Linked Data)
Watch the 10’ video tutorial here:


Basically, this spreadsheet will automatically calculate the jumping force-velocity profile from jump height, loads used and the anthropometrical data of the athlete (body mass, lower limb length) based on the equations validated in 2008 by Samozino et al. It will also display the optimal profile (i.e. the profile that would maximize jump height for this athlete) and the “force-velocity imbalance”, which will help design more effective, individualized training content. Links to all the papers are inserted in the spreadsheet in case you want to know more about the concepts, models, validations. This is definitely a science-based approach of jump training, and this applies to vertical but also inclined push-offs like sprint start in running or swimming (see my previous blog post on the latter topic).
This “profiling” test may be done with only 2 loaded jumps, but for more accuracy we recommend using 4 or 5 loads. The spreadsheet allows you to check this accuracy and decide your best practice.
As to the devices needed, well, it’s up to you, any device that accurately measures jump height may be used: force plate, Optojump, jumping mat, linear position transducers, iOS app MyJump2, etc… But one thing is important to keep in mind: there are systematic differences in jump height measurements between some of these devices (e.g. between force plate / MyJump and Optojump) for some technical reasons. Consequently, you should be consistent and always use the same device to monitor / compare athletes’ data over time, and you should not profile athletes based on jump height measured with one type of device and compare to data measured with another type of device.
In addition, note that the spreadsheet may also be used to compute countermovement jump FV profile, provided that the downward movement depth is controlled and accurately taken into account in the calculation of the starting height.
The following tips for a correct testing procedure (and thus a highly linear FV relationship and R2 close to 1) are based on our extensive use of the approach and hundreds of profile tests performed:
  • Make sure the athlete is familiar with loaded jumps up to additional mass close to his/her body mass. Several preparation sessions may be necessary, but it is worth it to ensure accurate and reproducible measurement.


  • A 5 to 10-minute general warm-up should be performed (e.g. running, cycling or rowing) followed by a specific warm-up with vertical jumps and a progressive increase in the intensity and loaded jumps, which contributes to avoid any apprehension of this kind of exercise and benefit from a possible potentiation effect.
  • We recommend 2 trials per load condition, and should jump height differ between the trials by more than 5%, a 3rd trial should be performed. The spreadsheet displays the profile “linearity” as you enter the loads-jump height so you can easily check what trial was “wrong” i.e. not aligned with the others
  • If the starting position is freely chosen (knee angle about 90°) by the athlete, they will get to this position very consistently so you may not have to thoroughly check for this starting height. One way to verify this is to ask the athlete during the warm-up to get to this positon with different loads on the shoulders. You will normally measure the same starting height if this is the preferred starting height of the athlete. It’s important to measure it after warm up since it is always quite different after compared to before warm-up. Our observation is that this starting height is very consistently reached if the athlete has chosen it as the most comfortable position, and if they are focused during the trials. So maybe not necessary to lose time systematically checking for this. But remember starting height influences push-off distance, which influences jump height…


  • The range of loads chosen should be as large as possible: from 0 kg (squat jump) to the load that leads to a jump height of about 10 cm. Typically, for a trained soccer player of 80 kg, we use 0, 20, 40 and 60 kg in a randomized order. For athletes used to strength training, the maximal additional load can be their body mass or even heavier.
  • In case of very strong/heavy athletes, the maximal load may be lower than body mass, if the range of load is large.
  • Note that the jumping FV profile is linear in nature, so we use absolute loads, which greatly simplifies testing procedures. It is not necessary to calculate and set relative loads (as % of body mass or % or squat 1RM) since the F and V values obtained will align on the same line as those obtained with absolute loads…so you can save time here.
  • Always remember the main sources of bias in the jumping technique: there should be no trunk movement priori to push-off; no downward countermovement; take-off with lower limb full extension and land in the same position (i.e. no knee flexion, feet in full plantar flexion) when jump height is obtained from aerial time; jump with all-out effort intention.



19 thoughts on “A spreadsheet for jump Force-Velocity-Power profiling

  1. Thank you so much for posting the calculations in excel, really helps my understanding of the analysis. I understand, precise anthropometrics are important for accurate calculation of force, but how how much accuracy do you think we lose if we extrapolate push off distance from just lower limb length while strictly enforcing 90 degree starting position? By just using limb length standards from the literature.


  2. Hi Thomas, thanks for your words,
    Hpo has an influence on performance and mechanical outputs, and we did a sensitivity analysis in this paper:


    that shows that Hpo has a 1:1 weight influence on jump height, so basically the %error in Hpo determination reports to the error in jump height computation…


  3. thank you, that helps a lot. I love the concept, my only reservations are in using it with weak coach:athlete ratios


  4. Hi JB,

    Thanks for this videos and post i found it great to help my understanding of the concept. which i going to use within my own line of work.
    I was wondering if you had any advice/tips for setting up the spreadsheet.



  5. Thank for the good topic,Thanks for your sharing.



  6. Hi Thomas, thanks for the feedback, in fact this approach is to be used with trained people and coaches/athletes familiar with this kind of exercise and testing, for sure


  7. This is a great resource! We are considering using this application for late stage knee injury rehabilitation. In this case, these athletes likely have side to side differences in their FV profiles (ie. injured leg vs uninjured). Has there been any discussion regarding testing the validity of doing a single leg loaded jump to tease this out?


  8. Hi, no, no publication that I'm aware of. Some students have worked on this as to the feasibility of this single leg testing but no publication…


  9. This comment has been removed by the author.


  10. Hi JB,

    Any chance you'd be able to send the spreadsheet if I don't have access to the download?



  11. Dear Alex, please send me an email to my Uni email (find it in the University of Nice directory),


  12. Cuvelier François 14 Jan 2020 — 08:52

    C’est extrêmement interréssant et utile, merci de partager vos connaissances!
    J’aimerais savoir si vous avez publié des ressources sur l’entrainement à proprement parler (périodisation, méthodes …) ? Pour pouvoir mettre en place un entraînement adapté à son profil force-vitesse.
    Merci beaucoup


    1. Bonjour, merci pour votre commentaire, nous avons à ce jour publié deus études qui incluent un programme d’entrainement, mais il est assez “simple et direct” en termes de programmation. Peut-etre que d’autres travaux à venir viendront compléter cet aspect. Les liens ci-dessous donnent accès à ces deux articles, ils sont en libre accès

      jb morin


  13. Hi JB¡

    I don’t really understand the relationship between FV profile for ballistic and sprinting performance. While in sprinting performance HZT-F0 is one of the main mechanical capability of lower limbs to increase performance, ballistic push off optimal profile for 30º is more oriented to velocity capabilities.
    The only logical conclusion i get is that this F0 belongs to VCT-F0 and not to HZT-F0. So, that will lead into a higher horizontal net force production and a lower vertical net production for first sprinting steps. Am I right?

    Thank you so much¡


    1. thanks for your comment Carlos, yes basically this 30° optimal computation applies to single push-off phases not to cyclic movements like sprinting. And overall in trained subjects the VTC and HZT F0s are not well correlated. But we use the analogy of a first step push to explain this 30° orientation


  14. Hi JB Morin,

    First I congratulate you for the great job.

    According to the image, the 100% imbalance should not be optimal. Why does it indicate that you should work hard. The second question has to do with what kind of force should I work on (maximum force or rate force develop)?

    Although I am unable to attach the image with the graph and data. I send you the following data generated by the chronojump software.

    The data are as follows:
    F0: -1,3 N
    V0: 0,08 m/s
    Pmax. -1,4 W
    Samozino & col. 2008-13:
    – Profile (90º): 0%
    – Imbalance: 100%
    Need to develop force

    Thanks for everything.


    1. Thank you, the reported data of F0 and V0 do not make sense at all, so there might be an error somewhere, maybe use the spreadhseet we’ve develop to enter your jump height, mass etc and see where data are not logical?


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