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Can you explain your reasoning? The internet says Russia has 12400 tanks, so it would seem like only a very small fraction of the total are being destroyed. You think that after several hundred more they'll be forced to withdraw?

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2 Factors make ORYX data difficult to track: [1] their data is presented in raw (analog) format, and [2] because their data is overwritten multiple times a day, historical data is erased which makes equipment loss trend lines invisible. I know of only 1 website that makes equipment trend lines visible: https://www.engineereddata.com/go-ukraine.html

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I do agree that Russian losses are substantial. However, I think they are lower than your and the Ukrainian estimate given here.

1. The majority of lost ground equipment documented on Oryx are from captured and abandoned vehicles. I think for these a lower than 1 multiplier should be used to adjust for the possibility of these being retaken. I suspect that these vehicles are in areas mostly controlled by the invading Russian forces, and they were left in a lot of cases only temporarily by intention (meaning: due to mechanical breakdown/lack of fuel the crew abandoned them, expecting and the maintenance companies are to recover them later; crew is sleeping/cooking in nearby locations etc.), or even in case the crew deserted, patrolling or incoming fresh Russian units may recover them.

2. There is very large discrepancy between Oryx and Ukrainian sources regarding air assets. It is difficult to indentify flying objects, but the flames and smoke of crashed helicopters or airplanes are visible from pretty far away. I suspect most of the reported losses are from overly optimistic ground to air missile operators, who thought they shot down a Russian jet, but actually they missed, or only destroyed a drone or a missile. Hence, I think for these we should assume Oryx is much more accurate.

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Very interesting. Do you plan on doing that calculation on Ukrainian losses and how long they can keep up. Given that they have significantly less tanks and equipment in general.

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Interesting article. Excited to see how your conditional prediction turns out.

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