Category War by Numbers

Density of Deployment in Ukraine

It appears that both sides have deployed between 300,000 to 617,000 troops in this war. Putin claimed 617,000 deployed in mid-December. To quote “The front line is over 2,000 kilometers long, there are 617,000 people in the conflict zone.” See: Putin Says Over 600K Russian Servicemen in Ukraine – The Moscow Times. Ukraine shortly afterwards stated it was 450,00. I tend to lean towards the lower figures. As Russian advances over the last six months have been fairly limited, I am guessing that Ukraniain deployment is at least 300,000. It is probably closer to 400,00. They have put out a few figures noticeably higher than this, but if this was the case (and they were deployed forward), then we probably would not be seeing many advances by the Russians. So most likely the deployed figures for both sides are between 300,000 to 450,000. Let’s just use the figure 450,000 for the sake of simplicity.

The effective front line of Ukraine is around 700 kilometers. See: The front is really not 1,200 kilometers long – rev. 1 – The Dupuy Institute. Ukraine obviously has to maintain troops in mobile positions from Chernihiv to Sumy, but there are probably forces still being stood up and trained, with their defense being supplemented by National Guard and Territorial Defense Forces, to be stood up as needed.  There is also the area opposite of the Khakhovka Reservoir, which is only light held by both sides. Then there is the area from the Dnipro River down to Kherson. This is an inactive front, because of the logistics issues caused by the river. While this does have to be held by forces on both sides, they basically have done no major operations since November 2022.  That will almost certainly be the case going forward. So, the active front is only around 700 kilometers (435 miles) 

S0, 450,000 divided by 700 km equals 643 troops per kilometer. This would be 429 per kilometer if there were only 300,000 troops. Obviously, they are not equally distributed across those 700 kilometers, but they really can’t leave large parts of the line seriously undermanned.

So, how does this compare to the last war in Ukraine (1941-1944)? 

During World War II, on the Western Front, the troops were often deployed to a density of 2,000 troops per kilometer of front line. On the Eastern Front in World War II, it was often over 1,000 troops per kilometer. Now we do have a division-level database of 752 cases. Of those, 267 are from the Eastern Front 1943-1945.  Let’s take a look at some examples from that:

For example, before the start of the Battle of Kursk the density of the front was (@ 1800, 4 July 1943):

  • 57th ID: 684 vs 683
  • 255th ID: 467 vs 495
  • 48th PzC (-): 2,458 vs 651
  • 11th PzD+: 1,976 vs 1,038
  • LSSAH GzGrD: 3,763 vs 1,261
  • DR SS PzGrD: 5,207 vs 899
  • T SS PzGrD: 2,416 vs 940
  • 6th PzD+: 2,282 vs 1,168
  • 19th PzD+: 6,086 vs 3,104
  • 7th PzD+: 2,766 vs 558
  • 106th ID: 2,419 vs 511
  • 320th ID: 2,572 vs 540

Just before the Battle of Prokhorovka we have the densities at (@1800, 11 July 1943):

  •  57th ID: 395 vs 483
  • 255th ID: 482 vs 399
  • 332nd ID+: 504 vs 463
  • 48th PZC (-): 1,694 vs 1,353
  • 11th PzD+: 1,669 vs 3,373
  • 167th ID: 725 vs 917
  • T SS PzGrD: 1,371 vs 782
  • LSSAH PzGrD: 2,904 vs 1,692
  • DR SS PrGrD: 1,851 vs 1,291
  • 168th ID: 1,430 vs 282
  • 19th PzD: 1,084 vs 195
  • 6th PzD: 2,077 vs 1,348
  • 7th PzD: 3,701 vs 1,743
  • 198th ID: 1,779 vs 669
  • 106th ID: 1,690 vs 1,658
  • 320th ID: 1,302 vs 1,032

Now, we do have engagements from the fighting around Kharkov in February, March and August of 1943. Some sample cases (again keying of the German unit:

15 February 1943:

  • GD ID: 888 vs 1,143
  • DR SS: 800 vs 1,794

12 March 1943:

  • LSSAH D: 753 vs 473
  • DR SS D: 2,205 vs 450
  • T SS D: 306 vs 2
  • 11th PzD: 914 vs 498

22 August 1943:

  • 106th ID: 1,341 vs 875
  • 320th ID: 1,007 vs 1,210


Now World War I was a lot more dense, especially on the western front. For example:

  • Br 8th Division, 1 July 1916: 8,071 vs 2000 (Battle of the Somme)
  • Dr. Fourth Army (-), 14 July 1916: 10,000 vs 3,333 (Somme)
  • U.S. 4th Bde (+), 6 June 1918: 2,145 vs 1,463 (Belleau Wood)
  • U.S. 3rd Bde, 1 July 1918: 7,118 vs 5,754
  • U.S. 2nd Bde (+), 12 September 1918: 11,007 vs 1,742.
  • U.S. 2nd Div (+), 3 October 1918: 4,063 vs 2,031
  • U.S. 36th Div, 8 October 1918: 4,500 vs 2,500

During the Arab-Israeli Wars we see a lower deployment density, for example, in the 16 engagements in our division-level database from the 1967 war, the densities (for offense) range from 813 to 3,567 men per kilometer (with four exceptions, Mitla Pass, Zaoura-Kala, Jerin and Kabtiya). In the 1973 war we have 32 division-level engagements.  The densities (for offense) range from 444 to 4,900. There are no outliers.

In the 1991 Gulf War, we also see a lower deployment density. In the 15 engagements in our division-level database we have the densities ranging from 89 to 1,200 men per kilometer.

Keep in mind this is a single dimension measurement of a two-dimensional construct. The units also deploy in depth. So, there is not one man standing there every two meters, any more than with a WWII density of 2,000 there are people standing shoulder-to-shoulder across the front line. The minority of troops deployed are shooters.

The main point is that the density is around a fourth of the typical density on the Western Front in WWII. And again, that is in one dimension.

I will leave this blog post without a conclusion, as I am not sure what it should be. For now, this is just an observation.

Army- and Division-level force ratio posts

I did five posts on analyzing force ratios using the campaign database. They are here:

Analysis for Force Ratios using the Campaign Data Base (CaDB) – The Dupuy Institute

Analysis for Force Ratios using the Campaign Data Base (CaDB) – continued – The Dupuy Institute

Analysis of Force Ratios using the Campaign Data Base (CaDB) – second continuation – The Dupuy Institute

Analysis of Force Ratios using the Campaign Data Base (CaDB) – third continuation – The Dupuy Institute

Analysis of Force Ratios using the Campaign Data Base (CaDB) – fourth and final continuation – The Dupuy Institute

 

I think this is actually kind of a big deal, and will be presenting it at HADSS in July: Updated Schedule for HADSS 2024 – The Dupuy Institute and at HAAC in October:  Next Revised Schedule for the Third Historical Analysis Annual Conference (HAAC), 8 – 10 October 2024 – The Dupuy Institute

 

Now, as part of that presentation, I do compare it to the division-level engagements. I have posted about this before. They are here:

The U.S. Army Three-to-One Rule versus the 752 Case Division-level Data Base 1904-1991 – The Dupuy Institute

The World War I Cases from the Division-level Database – The Dupuy Institute

The World War II Cases from the Division-level Database – The Dupuy Institute

Post-World War II Cases from the Division-level Database – The Dupuy Institute

Force Ratios at Kharkov and Kursk, 1943 – The Dupuy Institute

Force Ratios in the Arab-Israeli Wars (1956-1973) – The Dupuy Institute

 

And a summary of force ratios and 3-to-1 rule posts:

Summation of Human Factors and Force Ratio posts – The Dupuy Institute

Summation of Force Ratio Posts – The Dupuy Institute

JSTOR, Trevor Dupuy, Combat Data and the 3:1 Rule – The Dupuy Institute

 

And more stuff:

Force Ratios and CRTs – The Dupuy Institute

 

and most recently here: 

The 3-to-1 rule and the War in Ukraine – The Dupuy Institute

 

And in the first few chapters of my book War by Numbers.

 

Anyhow, we have discussed force ratios at the division-level and have now addressed them at the army-level by using the campaign databases. We do have the ability to look at them at Battalion and Company-level, which I will probably do at some point in the future. We do have a couple of databases to address this. They are no where near as robust as our division-level data base (752 cases) but as they are the only thing out there like that, they will have to do.

Battalion and Company Level Data Bases – The Dupuy Institute

At some point this will all probably be assembled in my future book More War by Numbers, which is half-written. Probably won’t get serious about that book until 2025. 

IDF Wounded-to-Killed Ratios

We have the following data for the Israeli Defense Forces from their website here: IDF Fallen and Wounded in War | ATC (www.idf.il).

Killed:

Killed since the beginning of the war (7 October 2023): 639

Killed: 290 dead (fighting in the Gaza Strip from 27 October 2023 to 29 May 2024 among those “whose names were permitted to be published”)

Now, 639 – 290 = 349 killed on 7 October or shortly thereafter.

Fatalities from operational accidents: 44 (this in 22 from 
two-sided shooting, 5 from “shooting anomalies” and 17 from “accidents”). Data from fatalities from operational accidents is correct as of 15 May 2024.

Now, I do not know if operational accidents are included in the war dead. I am assuming they are not, so 290 + 44 = 334 or 639 + 44 = 683

 

Wounded:

Wounded since the beginning of the war (this means from 7 October):

3,643

  • 2,124 “easy”
  • 955 “medum”
  • 564 “hard”

“Casualties” (do they mean wounded? – I assume so) from the beginning of the maneuver (this means from 23 October):

 1,831

  • 874 “easy”
  • 591 “medum”
  • 366 “hard”

 

Injuries:

Injuries for operational accidents in the Gaza Strip

714

  • Accidents: 453
  • Shooting anomalies: 36
  • Two-sided shooting: 57
  • Road accidents: 49
  • Other: 119

 

Okay, time for some simple math:

 

A. Wounded-to-killed ratios:

Overall force Wounded-to-killed ratios (not counting operational accidents): 3,643/639 = 5.33-to-1

Gaza Strip operations wounded-to-killed ratios: 1,831/290 = 6.31-to-1

7 October wounded-to-killed ratios: (3,643 – 1,831)/639-290) = 5.19-to-1

 

B. Accidental killed versus injures

Gaza Strip operations: 714/44 = 16.20-to-1

This is not a surprising figure, but not one that I have calculated before.

From “two-side shootings” and shooting anomalies: (57 + 30)/(22+5) = 3.20-to-1

From “two-sided shootings” (57/22) = 2.59-to-1

This are not surprising figures, being from I assume mostly direct gunfire.

 

C. How about friendly fire?

Percent killed by friendly fire in Gaza Strip: 22/(290 + 22) * 100 =  7.05 %

Note, the percent of expected friendly fire casualties has never been firmly established. Traditionally the figure from WWII was 1 or 2%. Many people considered these estimates low. It was clearly higher than that in Vietnam (1965-1973), but no one has assembled any systematic data. It was much higher than that in the Gulf War (1991).    

 

Some past references:

Wounded-To-Killed Ratios – The Dupuy Institute

Also note on page 187 of War by Numbers there is a discussion of weapons effects in the 1982 Israeli-Lebanon War.  The lethality figures of bullets was 0.31 and for “small arms” was 0.28. This comes out to wounded-to-killed ratios respectively of 3.23- and 3.57-to-1. 

French Estimate of Russian Killed in Ukraine

Seems like everyone in and out of NATO has their own estimate of Russian losses. The current French estimate, according to their foreign minister, is 150,000 Russian soldiers killed and a total of 500,000 casualties. See: France’s Shocking Estimation: 150,000 Russian Soldiers Dead in Ukraine War (msn.com)

First reality check: Wounded-to-killed ratios. 500,000 – 150,000 = 350,000 wounded. Wounded-to-killed ratio of 2.33-to-1. The Soviet Army mostly on the attack in the southern salient of the Battle of Kursk from 12-18 July 1943 had a wounded-to-killed ratio of 2.68-to-1 (see Kursk: The Battle of Prokhorovka, page 1374, this is also in Chapter 15 of War by Numbers).

Are they saying the Russian medical care is worse now than in 1943, before they had penicillin, or in many cases no painkillers other than Vodka? They also had in 1943 a shortage of trained doctors, the rear hospitals were not brought forward that spring to be near the front, and they had a poor medical evacuation system.

Anyhow, another estimate to ignore. What data are these estimates actually based upon?

Analysis of Force Ratios using the Campaign Data Base (CaDB) – fourth and final continuation

This is the fourth and final continuation of our previous four posts: Analysis for Force Ratios using the Campaign Data Base (CaDB) | Mystics & Statistics (dupuyinstitute.org) and Analysis for Force Ratios using the Campaign Data Base (CaDB) – continued | Mystics & Statistics (dupuyinstitute.org) and Analysis of Force Ratios using the Campaign Data Base (CaDB) – second continuation | Mystics & Statistics (dupuyinstitute.org) and Analysis of Force Ratios using the Campaign Data Base (CaDB) – third continuation | Mystics & Statistics (dupuyinstitute.org).  It is a part of a briefing on forces ratios I will be giving at HADSS in UK: Schedule for HADSS 2024 | Mystics & Statistics (dupuyinstitute.org) and at HAAC near DC: Next Revised Schedule for the Third Historical Analysis Annual Conference (HAAC), 8 – 10 October 2024 | Mystics & Statistics (dupuyinstitute.org)

All of this analysis of the CaDB was for a reason, it was to determine if odds (force ratios) play out difference at higher level of operations (meaning army level). Are they different at the operational level vice the tactical level of warfare. The answer appears to be no. I do not know of anyone who has actually specifically explored this issue before, so I am not sure there is an existing or countervailing opinions out there.

Of course, my real interesting in looking at this (which I did last year) was because of the war in Ukraine and the upcoming Ukranian spring/summer offensive in 2023. I did brief this at the Second HAAC (October 2023) and in Norway (November 2023). The question I had was does a minor advantage in force ratios or combat power ratios lead to a bigger advantage at the operational level of combat. The answer appears to be no, as this was reinforced by limited movement of the front line in Russo-Ukrainian War since the fall of 2022. 

My final slide in the briefing was “Does this relate to the fighting in Ukraine?” I then asked two questions:

  1. What are the odds?
    1. What is the strength of the deployed Ukrainian Army?
    2. What is the strength of the Russian Army deployed in Ukraine?
  2. What other advantages does the Ukrainian attacker have?
    1. Artillery
    2. Air Support? (Drones?)
    3. Observations/Intelligence
    4. Morale
    5. Training

Now, as it appears that Russia will be on the offensive this spring/summer, then I may need to restructure this slide and also add another point “artillery ammunition supply.”

 

I am probably going to do some more blog posts on this subject, looking at other levels of combat.

 

Analysis of Force Ratios using the Campaign Data Base (CaDB) – third continuation

This is a continuation of our previous three posts: Analysis for Force Ratios using the Campaign Data Base (CaDB) | Mystics & Statistics (dupuyinstitute.org) and Analysis for Force Ratios using the Campaign Data Base (CaDB) – continued | Mystics & Statistics (dupuyinstitute.org) and Analysis of Force Ratios using the Campaign Data Base (CaDB) – second continuation | Mystics & Statistics (dupuyinstitute.org). It is a part of a briefing on forces ratios I will be giving at HADSS in UK: Schedule for HADSS 2024 | Mystics & Statistics (dupuyinstitute.org) and at HAAC near DC: Next Revised Schedule for the Third Historical Analysis Annual Conference (HAAC), 8 – 10 October 2024 | Mystics & Statistics (dupuyinstitute.org)

This is a continuation of Section IV of the briefing titled “What is necessary to have a good chance of generating a breakthrough?”

Having put together a table in the last post of force ratios and exchange ratios by outcome, I decided to take a moment to look at each of these cases. Each of these 94 cases is a fully mapped out campaign, many that you have heard of.

First looking at the 29 cases that were coded outcome IV (attacker advances). The average force ratios were 2.69-to-1 and the average exchange ratios were 1.51-to-1:

Force Ratio    Notes

0.58                 HUSKY – US Invasion of Sicily (39 days)

1.05                 HUSKY – UK Invasion of Sicily (39 days)

1.15                 Ardennes Allied Counteroffensive South II (15 days)

1.22                SHINGLE – Allied Landing at Anzio (10 days)

1.23                The West Bank 1967 (3 days)

1.34                 Ardennes Allied Counteroffensive South I (9 days)

1.38                 Graziani’s Advance (6 days)

1.44                 Moselle-Metz (6 days)

1.50                 Ardennes Allied Counteroffensive North (15 days)

 

1.75 to 1.98     3 cases

2.02 to 2.32     4 cases

2.51 to 2.92     6 cases

3.63 to 4.94     5 cases

6.04 to 10.00   2 cases

 

What I was really looking for is to see if there is any pattern in these low odds cases. Do they represent particularly odd or unusual cases? They really don’t. It does help to look at the cases though.

I then looked at those 21 cases that were coded as outcome five (defender penetrated). The average force ratios were 2.75-to-1 and the average exchange ratios were 0.64-to-1. There did not seem to be any unusual pattern, although there are a number of Arab-Israeli cases in these low odd penetrations. That is because human factors matter (morale, training, experience, leadership, motivation, etc.). In fact, they matter a lot (and are not considered in most U.S. DOD combat models). 

Force Ratio   Notes

0.78                The Cauldron: Battle of Gazala (21 days)

0.80                The Sinai, 1967 (5 days)

0.93                Golan Heights, 1967 (2 days)

1.01                BUFFALO: Anzio Breakout (9 days)

1.50                KADESH: Israeli Attack in the Sinai (8 days)

1.57                PO Valley Breakthrough (UK) (22 days)

1.67                Battle of Normandy, US Army (31 days)

 

1.82 to 1.93    2 cases

2.10 to 2.49    3 cases

2.52 to 2.92    2 cases

3.47 to 4.54    5 cases

6.58 to 7.01    2 cases

 

By the way, if someone is looking for some 3-to-1 rule in this data, good luck. Warfare is more complex than that.

One more post to come on this series of force ratios for army-level operations. Debating what I should discuss next.

Analysis of Force Ratios using the Campaign Data Base (CaDB) – second continuation

This is a continuation of our previous two posts: Analysis for Force Ratios using the Campaign Data Base (CaDB) | Mystics & Statistics (dupuyinstitute.org) and Analysis for Force Ratios using the Campaign Data Base (CaDB) – continued | Mystics & Statistics (dupuyinstitute.org). It is a part of a briefing on forces ratios I will be giving at HADSS in UK: Schedule for HADSS 2024 | Mystics & Statistics (dupuyinstitute.org) and at HAAC near DC: Next Revised Schedule for the Third Historical Analysis Annual Conference (HAAC), 8 – 10 October 2024 | Mystics & Statistics (dupuyinstitute.org)

Section IV of the briefing is titled “What is necessary to have a good chance of generating a breakthrough?”

We coded some (94), but not all, of the 196 Army-level operations as to outcome. The outcomes are defined as (see War by Numbers for a more detailed description):

  • Outcome I is limited action
  • Outcome II is limited attack
  • Outcome III is failed attack
  • Outcome IV is attack advances
  • Outcome V is defender penetrated
  • Outcome VI is defender enveloped
  • Outcome VII is other.

These definitions are used to create the following table:

Outcome             I        II      III        IV       V       VI       VII

Cases                15        9     10        29      21      8          2

Force Ratios   1.88   3.35   1.80    2.69    2.75   1.86   8.50

Loss Ratios    3.77   1.56   1.66    1.51    0.64   0.05   0.01

 

Now, I put seven of those numbers in bold. They are worth looking at.

For those 10 operations that were coded as “failed attack”, the average force ratio is 1.80-to-1 while the average loss exchange ratio is 1.66-to-1 (i,e. the attacker lost more than the defender).

For those 29 operations that were coded as “attack advances”, the average force ratio is 2.69-to-1 while the average loss exchange ratio is 1.51-to-1.

For those 21 operations that were coded as “defender penetrated”, the average force ratio is 2.75-to-1 while the average loss exchange ratio is 0.64-to-1 (meaning the defender lost almost twice as many people as the attacker. Note that casualties included kill, wounded, missing and captured). 

One notices that the loss exchange ratio gets even more favorable in mop-up operations (defender enveloped). These are often the operation after “defender penetrated.”

A few other observations:

  1. Failed attacks tend to be lower average odds than successful ones (i.e. 1.80 versus 2.69 and 2.75).
  2. Attackers suffer higher losses than defenders until they are penetrated (1.61 and 1.51 versus 0.64)
  3. These are the same patterns as for division-level combat.

This last point is significant. Are operations with bodies of 60 thousand plus people the same as operations with 10-20 thousand people? At least in the patterns of force ratios required, loss exchange ratios, etc., they are very similar.

More to come (my briefings are long). The obvious next work step would be to finish coding the outcome of the other 102 operations in the CaDB. This is several man-weeks of effort. Not going to take that on now (I am trying to finish up another book).

Analysis for Force Ratios using the Campaign Data Base (CaDB) – continued

This is the continuation of our previous post: Analysis for Force Ratios using the Campaign Data Base (CaDB) | Mystics & Statistics (dupuyinstitute.org)

In that post was a table showing the force and losses differences between battles won by the attacker, the defenders and those that are drawn. Below is a follow-up table, showing the force ratios for all the campaigns:

Force Ratio      Attacker wins   Defender wins *   Draws **   Notes

0.30                    1                                                                  Suomussalmi

0.52 to 0.73        6                         2

0.77 to 1.00        7                         5

1.01 to 1.25      14                         3                            1

1.27 to 1.50        8                         3                            1

1.55 to 1.75        9                         3

1.78 to 2.00       11                        5

2.02 to 2.50       10                        6                             2

2.51 to 2.92         8                                                       1 ****

3.01 to 4.00         8                      4 ***                       1 ****    Loos (3.97) – defender wins

4.02 to 4.94         8

5.79 to 7.33         5

10.00 to 11.21     2

 

 

Notes:

* Removed from this seven engagements coded as “limited action” and “limited attack.” Their ratios were 0.58, 1.51, 2.90, 2.90, 3.58, 6.55, 12.38

** Removed from this 15 engagements coded as “limited action” and “limited attack.”

*** Three World War one engagements (Festubert at 3.01, Chemin des Dames at 3.33 and Loos at 3.97) and First Cassino (US) at 3.12.

**** Gothic Line Stalemate I at 2.58 and Gothic Line Statement II (US) at 3.08

 

These are slides 19 and 20 of my briefing. Now, I do not make conclusions on this slide in this briefing or even observations, but…. there are a few that could be made looking at this table. First, a three-to-one rule doesn’t really apply. Second, the defender never wins above four-to-one. Third, clearly there are a lot of factors included in these campaigns beyond simple manpower counts, and…. fourth…. you tell me?

The next slide of my briefing goes into the Section III of the briefing:  “Influence of Human Factors on Combat.” This is all drawn from War by Numbers… so… read the book. I will skip that and my next post will pick up at Section IV of the briefing “What is necessary to have a good chance of generating a breakthrough.” Probably do that post next Tuesday.

Analysis for Force Ratios using the Campaign Data Base (CaDB)

We have not made much use of our Campaign Data Base. (See: The History of the DuWar Data Bases | Mystics & Statistics (dupuyinstitute.org)). We used it as part of the Enemy Prisoner of War (EPW) studies back in 2000-2001 and have not made use it in the last two decades. But, for a presentation I did last year on force ratios, I blew the dust off of it because I wanted to see if force ratios were different for army-level operations than for division-level engagements. I mean, in the ETO data we have (116 cases), in the force ratios ranging between 1.15-to-1 to 1.88-to1 the attacker won 79% of the time (so much for needing 3-to-1). See: The 3-to-1 rule and the War in Ukraine | Mystics & Statistics (dupuyinstitute.org). So the question became, is the pattern we see at army-level different than division-level?

The Campaign Data Base consists of 196 campaigns from 1905 to 1991. They from two days in length to 155 days in length. Only three were over 60 days in length. The problem is that the database is not complete. We assembled it, used it once and have not used it again. There are some holes. For example, we only had the starting strength ratios calculated for 163 cases, we only had the total casualty ratios calculated for 162 and only had the winner calculated for 156 cases. In most cases the missing data is available but has not been assembled. The database just needs a little tender loving care. 

The average attacker strength (99 cases) was 188,909. The average defender strength (96 cases) was 95,497. This comes out to a 1.98-to-1 ratio.

The average attacker losses (176 cases) was 36,076. The average defender losses (172 case) was 47,004. This comes out to a 1-to-1.30 ratio.

The average attacker percent losses per day (163 cases) was 0.69%. The average defender percent losses per day (162 cases) was 1.85%. This comes out to a 1-to-2.68 ratio.

The starting strength ratio (163 cases) was 2.24 (2.24-to-1). The total casualty ratio was (164 cases) 1.35-to-1.

Now, the holes in the database become an issue. This are holes that can be filled given time (read: budget). We have 97 cases where the attacker is coded as the winner, and 38 cases where the defender wins. We have draws in 21 other cases. The rest (40 cases) are currently not coded.

Anyhow, this all produces the following table:

                                                   Attacker   Defender   Draw 

Av. Attacker Strength               208,835    156,821     171,312

Av. Defender Strength                91,486    100,729       96,582

       Ratio                                   2.28           1.56           1.77

 

Av. Attacker Losses                    34,630      69,098       15,232

Av. Defender Losses                   52,466      64,271       12,632

      Ratio                                     0.66           1.08           1.21

 

Av. Attacker % per day              0.73           0.98           0.32

Av. Defender % per day             2.59           0.98           0.39

      Ratio                                      0.28          1.00            0.82

 

Starting Strength Ratio              2.42          2.24            1.79

Casualty Ratio                            1.04          2.51            1.22

 

Contemplate for a moment what this data is telling you. A few observations:

  1. There is a difference in force ratios between winning and losing engagements (2.28-to-1 vice 1.56-to-1).
  2. There is a difference in casualties between winning and losing engagements (0.66-to-1 vice 1.08-to-1).
  3. The data for these army-level operations does not look significant different than for a division-level operation. This is significant.

I will stop here for a moment. This is from slides 12 – 18 for my force ratios briefing. There is more to come (because my briefings, like some of my books, are never short).

 

The 3-to-1 rule and the War in Ukraine

There is a 3-to-1 rule that some people quote from somewhere. We have discussed this before: Trevor Dupuy and the 3-1 Rule | Mystics & Statistics (dupuyinstitute.org) and The 3-to-1 Rule in Histories | Mystics & Statistics (dupuyinstitute.org) and The 3-to-1 Rule in Recent History Books | Mystics & Statistics (dupuyinstitute.org).

Trevor Dupuy’s argument was always that it took a combat power advantage to advance (attack successfully). This combat power calculations considers weapons, terrain, posture, air support, human factors, etc. Because of the current artillery shell shortages for the Ukrainian Army, logistics may also be a factor.

This combat power advantage often happens at 1.5-to-1 or 2-to-1. Usually is happens by around 2-to-1 (my conclusions – see War by Numbers). For example, here is my chart of force ratios for division-level combat in the European Theater of Operation (ETO) in 1944 from page 10 of War by Numbers:

FORCE RATIO…………………..RESULT……………..PERCENTAGE OF FAILURE………NUMBER OF CASES

0.55 TO 1.01-TO-1.00…………ATTACK FAILS………………………….100……………………………………5

1.15 TO 1.88-TO-1.00…………ATTACK USUALLY SUCCEEDS………21…………………………………..48

1.95 TO 2.56-TO-1.00…………ATTACK USUALLY SUCCEEDS………10…………………………………..21

2.71 TO 1.00 AND HIGHER….ATTACK ADVANCES……………………..0…………………………………..42

 

Notice that the attacker succeeds at force ratios between 1.15-to-1 to 1.88-to-1 in 79% of the 48 cases of division-level combat. It gets better from there. The book also has force ratios from other theaters and campaigns. Some of this has been discussed here before: More Combat Results Tables from War by Numbers | Mystics & Statistics (dupuyinstitute.org) and Force Ratios at Kharkov and Kursk, 1943 | Mystics & Statistics (dupuyinstitute.org) and Force Ratios in the Arab-Israeli Wars (1956-1973) | Mystics & Statistics (dupuyinstitute.org).

A rigidly defined 3-to-1 rule tends to create an officer corps of McLellan’s. This rule-of-thumb is doing more damage than good as constructed.

What got my attention is that some people are trying to apply some 3-to-1 rule in Ukraine, and then come to the conclusion that one or the other side cannot advance because they don’t have a 3-to-1 force ratio. Yet, people have been advancing. In fall of 2022 Ukraine re-took Kherson and surrounding areas (see: 2022 Kherson counteroffensive – Wikipedia) and achieved a breakthrough at Balakliya that took back a significant portion of Donetsk province (see: Battle of Balakliia – Wikipedia) and conducted a successful offensive around Kharkiv (see: 2022 Kharkiv counteroffensive – Wikipedia). In 2023 Russia did advance on Bakhmut and took it (see: Battle of Bakhmut – Wikipedia) and in 2023/2024 Russia did advance on Avdiivka and took it (see: Battle of Avdiivka (2023–2024) – Wikipedia). I think in three for those five cases the attacker did not have anything approaching a 3-to-1 advantage. Of course, I have no reliable manpower statistics for either side in any of these five battles, so this is sort of a guess, as is most of the analysis and expert opinions on this war. 

I do not know how many troops Ukraine currently has. I am guessing at least 300,000 deployed. Some people throw out figures in the 600-700,000 range. I have no idea if that are total mobilized estimates or total deployed estimates. The same with Russia, where figures of 600-700,000 are also thrown out, but not sure that is what is actually deployed in Ukraine. I am guessing some number closer to 300,000. Don’t really know, and don’t know who does for certain (see the “Force Involved’ section of this post: The Russo-Ukrainian War – Day 699 | Mystics & Statistics (dupuyinstitute.org)).

Anyhow, I gather the two sides are somewhere near parity in force size. They can certainly concentrate forces to get a local advantage. With current modern intelligence gathering capabilities, concentrating forces is often seen while it is happening and opposing side can respond promptly. So not sure where anyone can get their 3-to-1 advantage.

I did do a test recently, comparing the force ratios in a database over 700 division-level combat engagements to the force-ratios in over 100 Army-level operations. The question was whether force ratios and the success from those force ratios was different at division-level vice army-level. My tentative conclusions were that force ratios for army level campaigns had the “Same patterns as for division-level combat.”

Now, I have not written this effort up. I did brief it last year at the Second HAAC and did brief it in Norway. I will be briefing it again on Thursday, July 11 at HADSS in York (see:  Historical Analysis for Defence and Security Symposiums (HADSS), 8 – 11 July in York, England | Mystics & Statistics (dupuyinstitute.org)) and for one last time at the Third HAAC (see: Revised Schedule for the Third Historical Analysis Annual Conference (HAAC), 8-10 October 2024 | Mystics & Statistics (dupuyinstitute.org)). After that, I may write it up, either as a blog post or as a chapter in a book called More War By Numbers, which will probably be delayed until 2026 (see: Current book release schedule | Mystics & Statistics (dupuyinstitute.org), which I probably need to update).

Anyhow, the point is, anyone doing analysis for the situation in Ukraine based upon some 3-to-1 rule probably needs to reconsider their analysis.