A Data Science Approach To The Bermuda Triangle

A Data Science Approach To The Bermuda Triangle Current records affirm that the frequency of disappearances in the Bermuda Triangle is not a statistical anomaly. But aberrant environmental and psychological factors likely exist in some cases that deserve more scrutiny.


A fresh analysis of marine and aviation casualty statistics (1982-2015) from the US Coast Guard affirmed that the frequency of disappearances within the ‘Bermuda Triangle’ is not high compared to other regions. Although this result supports previous characterizations of this topic as a ‘manufactured mystery,’ this classic Fortean legend can inspire new studies on the legitimate issues of (a) periodic meteorological or environmental phenomena that can disrupt navigation safety and (b) circumstances or conditions that mediate people feeling or becoming physically lost due to unusual spatial disorientation effects.

Keywords: Bermuda Triangle, disappearances, disorientation, environmental anomalies, myth


Popularized by Charles Berlitz’s (1974) best-selling book and reinforced by a subsequent plethora of texts, movies, and TV programs, the so-called ‘Bermuda Triangle’ is infamous for its high-profile and seemingly inexplicable air- and sea-based disappearances (e.g., Begg, 1979/1981; for recent overviews and evidence-based discussions, see Kevin Randle’s blog “A Different Perspective”: http://kevinrandle.blogspot.com). According to Britannica.com, this target area of the North Atlantic Ocean —also known as the ‘Devil’s Triangle — is loosely bounded by the southeastern coast of the U.S., Bermuda, and the islands of the Greater Antilles (Cuba, Hispaniola, Jamaica, and Puerto Rico). Reports of unexplained occurrences in the region date to the mid-19th century, with more than fifty ships and twenty airplanes reputedly having vanished without a trace. Despite its enduring supernatural allure, it is debatable that the frequency of losses actually constitutes a statistical anomaly. To address this, we examined the aviation and marine casualty data over the last few decades to make quantitative comparisons between this region and elsewhere in the globe.  

Method and Analysis

Marine Casualties: The data available online are mostly fragmented by regions or have limited  timespan. The most comprehensive one we found relevant to this analysis was the ‘Marine Casualty and  Pollution Data for Researchers’ dataset provided by the United States Coast Guard (150,000+ records, spans 1982-2015). This dataset reports only investigations pursued by the Coast Guard pertaining to US-related activities, which is a source of bias, but it remains suitable for our usage as it covers the Bermuda region, as well as other oceanic regions around the globe. We pre-processed the dataset via Python, and specifically the geopandas and matplotlib library for visualizing the map, omitting some records due to the presence of typos, formatting mismatch  and missing data. Figure 1 shows the final result.  

Figure 1 Marine Casualty & Pollution Data for Researchers, all reported incidents within the Bermuda Triangle (red) and  elsewhere (green). Disappearance incidents are marked with their last known locations. 

It is apparent from this Figure that the reported incidents are clustered around coastal areas  (further illustrated in Fig. 2), as they account for the majority of collision events and accidents that occur while disembarked. Since we are primarily interested in studying the conditions of ocean regions, only incidents that happen at least 80 miles (> 80th percentile) from the nearest coast are considered. 

Figure 2 The distance to land frequency graph shows a quickly decaying pattern. Generated using land geometry data provided  by the geopandas package. 

A total of 9,096 incidents are considered, 361 of which fall within the Triangle (defined as the area spanning Miami, Bermuda, and San Juan, Puerto Rico). Qualitatively, we can detect the presence of unusual activity by comparing the causes of casualties that occur within the Triangle and elsewhere. As shown in Fig. 3, the types of incidents in this region mostly resemble the worldwide pattern, with an expected lower share of capsizing events possibly due  to its proximity to major trading ports. Interestingly, there was no incidence classified under ‘Disappearance’ reported within the Bermuda Triangle from 1982-2015. 

Figure 3 Cause of casualties in Bermuda Triangle (left) and elsewhere (right)  

Furthermore, we can determine whether the frequency of incidents within this region falls within  an expected range. To this end, kernel density estimation (KDE) is utilized to model the underlying  distribution of the observed data (shown in Fig. 4). This allows us to visualize the density of data  points around the globe and identify regions with particularly high rates of marine incidents. 

Figure 4 Kernel Density Estimation plot, with parameters chosen to minimize cross-validation error 

The KDE plot indicates that naval incidents are particularly concentrated around three regions:  The Gulf of Mexico, the Gulf of Alaska and the north-western part of the Pacific Ocean.  The Bermuda Triangle is situated on the eastern edge of the Gulf of Mexico, which could imply that  the Triangle itself is not an isolated region of unusual activity but rather characteristic of the  marine region around the southeastern United States. What remains unanswered, however, is whether this  concentration of incidents is due to high marine traffic or the severity of the weather conditions.  

To explore these factors, the next step was to integrate data on global shipping traffic into the analysis. Unfortunately, both the governmental and private data sources (‘Offshore Waters Vessel Traffic Data’, www.MarineTraffic.com) are hidden behind a paywall and thus are not readily accessible. We can however still utilize meteorological data to infer the influence of weather on  incidents within the Triangle. The chart below illustrates the seasonal effect of tropical storms on capsizing and collision events — both of which are commonly cited as explanations for missing  ships. With a Pearson correlation of 0.71 (p < 0.01), the marine incidents do indeed exhibit a strong correlation with adverse weather conditions, giving some credence to the explanation.  

Figure 5 Number of tropical storms affecting the Bermuda island (bar) compared with a subset of marine incidents (line) 

Aviation Casualties: With notorious incidents such as the disappearance of the Flight 19  bombers and multiple passenger aircrafts, aviation incidents in the Triangle merit closer inspection. We found the ‘Aviation Accident Database’ provided by the National Transportation Safety Board to be suitable for this task, as it covers all investigated accidents (by US authorities) from 1962 up to present, inclusive of both commercial and military flights.  

US Navy Avengers, similar to those of Flight 19

The low rates of incidents within the Triangle during this period presents difficulty in drawing  any firm conclusions based on quantitative comparisons. As listed in Table 1, of the 53 incidents  recorded within its boundaries, the vast majority occurs during landing/take-off (particularly in airports in the Bahamas), and thus are not directly linked to our investigation.  Of the 11 incidents that occurred during the journey, only five lead to fatalities and two are missing, both of which were personal flights by a single pilot. The only documented commercial flight accident during this period is attributed to the pilot’s poor judgment of the weather conditions.  

Bermuda Triangle Elsewhere

Total Recorded 53 84,216 

Take-off/Landing 42 64,471 

Personal Flights 5 11,625 

Cruising/Others 11 19,745 

Cruising/Others Fatal Incidents 5 6,548 

Cruising/Others Destroyed   4  6,080 

Table 1 Statistics of incidents occurring within the Bermuda Triangle and elsewhere (landing and takeoff excluded),  


Combining the insights from analyzing the available marine and aviation casualties from the past several decades, the Bermuda Triangle does not appear to be a particularly anomalous region. This finding corroborates the World Wildlife Fund’s (2013) exhaustive study of maritime shipping lanes that determined the Triangle was not one of the world’s ten most dangerous bodies of water for shipping. Indeed, all the past notable incidents in the area were reported during the pre- radar and -satellite weather tracking eras, so it seems quite likely that capsizing events and flight accidents were simply not communicated, and their wreckage were conceivably swept away by the currents. Regardless, our results agree with Kusche’s (1975/1995) seminal analysis and critique of notable incidents that are traditionally cited in support of paranormal interpretations. Other authors have similarly concluded that the Triangle is not a legitimate anomaly (e.g., Begg, 1979/1981; Raine, 1997; Rosenberg, 1974), including one study that found no relationship between incidents in the Triangle and UFO sightings (Prytz, 1980). Of course, the authenticity of publicly available data is itself an uncertainty, and further steps can be taken to cross-validate with alternative data such as marine/aviation traffic density, accident reports, and trends in the media coverage to detect any incoherence.  


The Bermuda Triangle narrative involves two critical aspects: (a) the frequency of disappearances and (b) the nature of those events. It can be argued that analyses like ours show that nothing is inherently anomalous about the frequency of the losses, but the issue of attenuating circumstances or context is another question altogether. We found that nautical disappearances highly correlated with adverse weather conditions, whereas the low sample of aerial events in this geographic areas prevents a statistical analysis. It should be noted that some case reports implicate environmental or meteorological phenomena at certain times or under particular conditions within the Triangle. Anecdotal accounts have described odd vortex-like manifestations and electromagnetic disruptions to instrument panels (for a discussion, see Kusche, 1975/1995). Indeed, at least three conventional factors have been assessed in the peer-reviewed literature as viable contributing factors to losses in this region: (a) hexagonal clouds that create ‘air bombs’ (Walter, 2016), (b) diffraction of heat waves (Njau, 1995), and (c) methane gas hydrates that can sink plans and ships (Appenzeller, 1991; Cusack, 2018; Dillon, n.d.; Gruy, 1998; May & Monaghan, 2003). 

There are also people who have apparently experienced a feeling of being ‘lost’ while in the Triangle (Winer, 1974, 1976, 1977). This could suggest the role of perceptual or cognitive issues during nautical and aerial navigation. Future studies should accordingly examine the prevalence and impact of ‘nature shock’ in other than land-based settings, i.e., when an individual’s physical or mental conceptions of space become disjointed (Coleman, 2020). This can occur progressively (Sargent et al., 2008) or involve individuals affected by Developmental Topographical Disorientation, i.e., “a lifelong selective inability to orient despite otherwise well-preserved general cognitive functions, and the absence of any acquired brain injury or neurological condition” (Burles & Iaria, 2020, para 1.). Select cases might therefore deserve further scientific scrutiny to find comprehensive explanations for all aspects of the various reports. But as it stands, we conclude that the Bermuda Triangle’s sinister reputation rests more on sensationalism than actual anomalies.

Tracks of all Atlantic hurricanes between 1851 and 2019. Many storms pass through the Bermuda Triangle.

Implications and Applications

The Bermuda Triangle is more than a case study of myth-making and narrative development (cf. Cochran-Smith, 2003), as our analysis revealed that cluster regions such as the Gulf of Alaska and the northwestern Pacific Ocean need closer examination due to their ostensibly high number of incidents despite being situated in open waters. Data science can analyze the causes of marine and aviation casualties in conjunction with meteorological data to find ways to reduce these losses. Our study further underscores the need for more transparency and efficiency with data-sharing, especially given the availability of big data that is amenable to powerful analytical techniques. Mining this information can help researchers of certain controversial issues to separate the signal quickly and convincingly from the proverbial noise.


We thank Cindy Little for assistance with background information used in this report and the reviewers’ feedback on an earlier draft. This study was supported by Rebellion Research (https://www.rebellionresearch.com).


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